Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Documento de trabajo n° 2: Trayectorias escolares e inclusión de niños y jóve...Vanesa Ferrara
El objetivo de este documento es reflexionar acerca de las trayectorias educativas de niños, jóvenes y adultos con discapacidad o alguna restricción para el aprendizaje o la participación por el sistema educativo formal y no formal.
El recorrido por el sistema educativo formal de cualquier niño o joven se organiza en base una serie de indicadores que organizan cohortes teóricas. Cualquier desviación de las mismas es indicio de algún tipo de falla, y que en todos los casos se significa como marca de anormalidad o un fracaso del alumno.
A su vez estos indicadores que se construyen en base a dispositivos o formatos escolares clásicos se han naturalizado en las representaciones sociales acerca del tránsito posible para la infancia y la juventud. Al decir de Terigi, “el sistema educativo define, a través de su organización y sus determinantes, lo que llamamos trayectorias escolares teóricas. Las trayectorias teóricas expresan itinerarios en el sistema que siguen la progresión lineal prevista por éste en los tiempos marcados por una periodización estándar” (Terigi, 2007).
Contrariamente, las trayectorias escolares reales expresan los modos en que gran parte de los niños y jóvenes transitan su escolarización. Por cierto, mediante “modos heterogéneos, variables y contingentes” (Terigi, 2007). Estos modos se asocian, según la autora con la relativa inflexibilidad de nuestros desarrollos pedagógico- didácticos para dar respuestas eficaces frente a la diferencia.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Documento de trabajo n° 2: Trayectorias escolares e inclusión de niños y jóve...Vanesa Ferrara
El objetivo de este documento es reflexionar acerca de las trayectorias educativas de niños, jóvenes y adultos con discapacidad o alguna restricción para el aprendizaje o la participación por el sistema educativo formal y no formal.
El recorrido por el sistema educativo formal de cualquier niño o joven se organiza en base una serie de indicadores que organizan cohortes teóricas. Cualquier desviación de las mismas es indicio de algún tipo de falla, y que en todos los casos se significa como marca de anormalidad o un fracaso del alumno.
A su vez estos indicadores que se construyen en base a dispositivos o formatos escolares clásicos se han naturalizado en las representaciones sociales acerca del tránsito posible para la infancia y la juventud. Al decir de Terigi, “el sistema educativo define, a través de su organización y sus determinantes, lo que llamamos trayectorias escolares teóricas. Las trayectorias teóricas expresan itinerarios en el sistema que siguen la progresión lineal prevista por éste en los tiempos marcados por una periodización estándar” (Terigi, 2007).
Contrariamente, las trayectorias escolares reales expresan los modos en que gran parte de los niños y jóvenes transitan su escolarización. Por cierto, mediante “modos heterogéneos, variables y contingentes” (Terigi, 2007). Estos modos se asocian, según la autora con la relativa inflexibilidad de nuestros desarrollos pedagógico- didácticos para dar respuestas eficaces frente a la diferencia.
El maestro ignorante de Rancière en la sociedad digital Carlos Magro Mazo
Presentación empleada en la comunicación "Le Maître ignorant en la sociedad digital: nuevos medios para la emancipación intelectual" presentada por Esteban Romero y Carlos Magro el 10 de diciembre de 2014 dentro del congreso "Políticas de la Literatura" celebrado en la Universidad de Granada.
Seminario de Instituciones Educativas
presentacion realizada por Natalia Mataluna y Pablo Padula
------------------------------------------
TACIE
Taller de Analisis de la Comunicacion en las Instituciones Educativas --
Profesorado en Comunicacion Social --
Facultad de Periodismo y Comunicación Social --
Universidad Nacional de La Plata --
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Jack
1. ARTIFICIAL NEURAL NETWORKS AND ITS
APPLICATIONS
Girish Kumar Jha
I.A.R.I., New Delhi-110 012
girish_iasri@rediffmail.com
Introduction
Neural networks have seen an explosion of interest over the last few years and are being
successfully applied across an extraordinary range of problem domains, in areas as diverse as
finance, medicine, engineering, geology, physics and biology. The excitement stems from the
fact that these networks are attempts to model the capabilities of the human brain. From a
statistical perspective neural networks are interesting because of their potential use in
prediction and classification problems.
Artificial neural networks (ANNs) are non-linear data driven self adaptive approach as
opposed to the traditional model based methods. They are powerful tools for modelling,
especially when the underlying data relationship is unknown. ANNs can identify and learn
correlated patterns between input data sets and corresponding target values. After training,
ANNs can be used to predict the outcome of new independent input data. ANNs imitate the
learning process of the human brain and can process problems involving non-linear and
complex data even if the data are imprecise and noisy. Thus they are ideally suited for the
modeling of agricultural data which are known to be complex and often non-linear.
A very important feature of these networks is their adaptive nature, where “learning by
example” replaces “programming” in solving problems. This feature makes such
computational models very appealing in application domains where one has little or
incomplete understanding of the problem to be solved but where training data is readily
available.
These networks are “neural” in the sense that they may have been inspired by neuroscience
but not necessarily because they are faithful models of biological neural or cognitive
phenomena. In fact majority of the network are more closely related to traditional
mathematical and/or statistical models such as non-parametric pattern classifiers, clustering
algorithms, nonlinear filters, and statistical regression models than they are to neurobiology
models.
Neural networks (NNs) have been used for a wide variety of applications where statistical
methods are traditionally employed. They have been used in classification problems, such as
identifying underwater sonar currents, recognizing speech, and predicting the secondary
structure of globular proteins. In time-series applications, NNs have been used in predicting
stock market performance. As statisticians or users of statistics, these problems are normally
solved through classical statistical methods, such as discriminant analysis, logistic regression,
Bayes analysis, multiple regression, and ARIMA time-series models. It is, therefore, time to
recognize neural networks as a powerful tool for data analysis.
2. Artificial Neural Networks and its Applications
Characteristics of neural networks
• The NNs exhibit mapping capabilities, that is, they can map input patterns to their
associated output patterns.
• The NNs learn by examples. Thus, NN architectures can be ‘trained’ with known
examples of a problem before they are tested for their ‘inference’ capability on
unknown instances of the problem. They can, therefore, identify new objects
previously untrained.
• The NNs possess the capability to generalize. Thus, they can predict new outcomes
from past trends.
• The NNs are robust systems and are fault tolerant. They can, therefore, recall full
patterns from incomplete, partial or noisy patterns.
• The NNs can process information in parallel, at high speed, and in a distributed
manner.
Basics of artificial neural networks
The terminology of artificial neural networks has developed from a biological model of the
brain. A neural network consists of a set of connected cells: The neurons. The neurons receive
impulses from either input cells or other neurons and perform some kind of transformation of
the input and transmit the outcome to other neurons or to output cells. The neural networks
are built from layers of neurons connected so that one layer receives input from the preceding
layer of neurons and passes the output on to the subsequent layer.
A neuron is a real function of the input vector ( y1 ,K, y k ) . The output is obtained as
f ( x j ) = f ⎛α j + ∑i=1 wij y j ⎞ , where
k
⎜ ⎟ f is a function, typically the sigmoid (logistic or tangent
⎝ ⎠
hyperbolic) function. A graphical presentation of neuron is given in figure below.
Mathematically a Multi-Layer Perceptron network is a function consisting of compositions of
weighted sums of the functions corresponding to the neurons.
Figure: A single neuron
V-42
3. Artificial Neural Networks and its Applications
Neural networks architectures
An ANN is defined as a data processing system consisting of a large number of simple highly
inter connected processing elements (artificial neurons) in an architecture inspired by the
structure of the cerebral cortex of the brain. There are several types of architecture of NNs.
However, the two most widely used NNs are discussed below:
Feed forward networks
In a feed forward network, information flows in one direction along connecting pathways,
from the input layer via the hidden layers to the final output layer. There is no feedback
(loops) i.e., the output of any layer does not affect that same or preceding layer.
Input layer Hidden layer Output layer
Figure: A multi-layer feed forward neural network
Recurrent networks
These networks differ from feed forward network architectures in the sense that there is at
least one feedback loop. Thus, in these networks, for example, there could exist one layer with
feedback connections as shown in figure below. There could also be neurons with self-
feedback links, i.e. the output of a neuron is fed back into itself as input.
Input layer Hidden layer Output layer
Figure: A recurrent neural network
V-43
4. Artificial Neural Networks and its Applications
Learning/Training methods
Learning methods in neural networks can be broadly classified into three basic types:
supervised, unsupervised and reinforced.
Supervised learning
In this, every input pattern that is used to train the network is associated with an output
pattern, which is the target or the desired pattern. A teacher is assumed to be present during
the learning process, when a comparison is made between the network’s computed output and
the correct expected output, to determine the error. The error can then be used to change
network parameters, which result in an improvement in performance.
Unsupervised learning
In this learning method, the target output is not presented to the network. It is as if there is no
teacher to present the desired patterns and hence, the system learns of its own by discovering
and adapting to structural features in the input patterns.
Reinforced learning
In this method, a teacher though available, does not present the expected answer but only
indicates if the computed output is correct or incorrect. The information provided helps the
network in its learning process. A reward is given for a correct answer computed and a
penalty for a wrong answer. But, reinforced learning is not one of the popular forms of
learning.
Types of neural networks
The most important class of neural networks for real world problems solving includes
• Multilayer Perceptron
• Radial Basis Function Networks
• Kohonen Self Organizing Feature Maps
Multilayer Perceptrons
The most popular form of neural network architecture is the multilayer perceptron (MLP). A
multilayer perceptron:
• has any number of inputs.
• has one or more hidden layers with any number of units.
• uses linear combination functions in the input layers.
• uses generally sigmoid activation functions in the hidden layers.
• has any number of outputs with any activation function.
• has connections between the input layer and the first hidden layer, between the hidden
layers, and between the last hidden layer and the output layer.
Given enough data, enough hidden units, and enough training time, an MLP with just one
hidden layer can learn to approximate virtually any function to any degree of accuracy. (A
statistical analogy is approximating a function with nth order polynomials.) For this reason
MLPs are known as universal approximators and can be used when you have little prior
knowledge of the relationship between inputs and targets. Although one hidden layer is
always sufficient provided you have enough data, there are situations where a network with
two or more hidden layers may require fewer hidden units and weights than a network with
one hidden layer, so using extra hidden layers sometimes can improve generalization.
V-44
5. Artificial Neural Networks and its Applications
Radial Basis Function Networks
Radial basis functions (RBF) networks are also feedforward, but have only one hidden layer.
A RBF network:
• has any number of inputs.
• typically has only one hidden layer with any number of units.
• uses radial combination functions in the hidden layer, based on the squared Euclidean
distance between the input vector and the weight vector.
• typically uses exponential or softmax activation functions in the hidden layer, in which
case the network is a Gaussian RBF network.
• has any number of outputs with any activation function.
• has connections between the input layer and the hidden layer, and between the hidden
layer and the output layer.
MLPs are said to be distributed-processing networks because the effect of a hidden unit can
be distributed over the entire input space. On the other hand, Gaussian RBF networks are said
to be local-processing networks because the effect of a hidden unit is usually concentrated in a
local area centered at the weight vector.
Kohonen Neural Network
Self Organizing Feature Map (SOFM, or Kohonen) networks are used quite differently to the
other networks. Whereas all the other networks are designed for supervised learning tasks,
SOFM networks are designed primarily for unsupervised learning (Patterson, 1996).
At first glance this may seem strange. Without outputs, what can the network learn? The
answer is that the SOFM network attempts to learn the structure of the data. One possible use
is therefore in exploratory data analysis. A second possible use is in novelty detection. SOFM
networks can learn to recognize clusters in the training data, and respond to it. If new data,
unlike previous cases, is encountered, the network fails to recognize it and this indicates
novelty. A SOFM network has only two layers: the input layer, and an output layer of radial
units (also known as the topological map layer).
Figure: A Kohonen Neural Network Applications
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6. Artificial Neural Networks and its Applications
Neural networks have proven to be effective mapping tools for a wide variety of problems and
consequently they have been used extensively by practitioners in almost every application
domain ranging from agriculture to zoology. Since neural networks are best at identifying
patterns or trends in data, they are well suited for prediction or forecasting applications. A
very small sample of applications has been indicated in the next paragraph.
Kaastra and Boyd (1996) developed neural network model for forecasting financial and
economic time series. Kohzadi et al. (1996) used a feedforward neural network to compare
ARIMA and neural network price forecasting performance. Dewolf et al. (1997, 2000)
demonstrated the applicability of neural network technology for plant diseases forecasting.
Zhang et al. (1998) provided the general summary of the work in ANN forecasting, providing
the guidelines for neural network modeling, general paradigm of the ANNs especially those
used for forecasting, modeling issue of ANNs in forecasting and relative performance of
ANN over traditional statistical methods. Sanzogni et al. (2001) developed the models for
predicting milk production from farm inputs using standard feed forward ANN. Kumar et al.
(2002) studied utility of neural networks for estimation of daily grass reference crop
evapotranspiration and compared the performance of ANNs with the conventional method
used to estimate evapotranspiration. Pal et al. (2002) developed MLP based forecasting model
for maximum and minimum temperatures for ground level at Dum Dum station, Kolkata on
the basis of daily data on several variables, such as mean sea level pressure, vapour pressure,
relative humidity, rainfall, and radiation for the period 1989-95. Gaudart et al. (2004)
compared the performance of MLP and that of linear regression for epidemiological data with
regard to quality of prediction and robustness to deviation from underlying assumptions of
normality, homoscedasticity and independence of errors. The details of some of the
application in the field of agriculture will be discussed in the class.
The large number of parameters that must be selected to develop a neural network model for
any application indicates that the design process still involves much trial and error. The next
section provides a practical introductory guide for designing a neural network model.
Development of an ANN model
The various steps in developing a neural network model are:
A. Variable selection
The input variables important for modeling variable(s) under study are selected by suitable
variable selection procedures.
B. Formation of training, testing and validation sets
The data set is divided into three distinct sets called training, testing and validation sets. The
training set is the largest set and is used by neural network to learn patterns present in the
data. The testing set is used to evaluate the generalization ability of a supposedly trained
network. A final check on the performance of the trained network is made using validation
set.
C. Neural network architecture
Neural network architecture defines its structure including number of hidden layers, number
of hidden nodes and number of output nodes etc.
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7. Artificial Neural Networks and its Applications
• Number of hidden layers: The hidden layer(s) provide the network with its ability to
generalize. In theory, a neural network with one hidden layer with a sufficient number of
hidden neurons is capable of approximating any continuous function. In practice, neural
network with one and occasionally two hidden layers are widely used and have to
perform very well.
• Number of hidden nodes: There is no magic formula for selecting the optimum number
of hidden neurons. However, some thumb rules are available for calculating number of
hidden neurons. A rough approximation can be obtained by the geometric pyramid rule
proposed by Masters (1993). For a three layer network with n input and m output
neurons, the hidden layer would have sqrt(n*m) neurons.
• Number of output nodes: Neural networks with multiple outputs, especially if these
outputs are widely spaced, will produce inferior results as compared to a network with a
single output.
• Activation function: Activation functions are mathematical formulae that determine the
output of a processing node. Each unit takes its net input and applies an activation
function to it. Non linear functions have been used as activation functions such as
logistic, tanh etc. The purpose of the transfer function is to prevent output from reaching
very large value which can ‘paralyze’ neural networks and thereby inhibit training.
Transfer functions such as sigmoid are commonly used because they are nonlinear and
continuously differentiable which are desirable for network learning.
D. Evaluation criteria
The most common error function minimized in neural networks is the sum of squared errors.
Other error functions offered by different software include least absolute deviations, least
fourth powers, asymmetric least squares and percentage differences.
E. Neural network training
Training a neural network to learn patterns in the data involves iteratively presenting it with
examples of the correct known answers. The objective of training is to find the set of weights
between the neurons that determine the global minimum of error function. This involves
decision regarding the number of iteration i.e., when to stop training a neural network and the
selection of learning rate (a constant of proportionality which determines the size of the
weight adjustments made at each iteration) and momentum values (how past weight changes
affect current weight changes).
Software
Several software on neural networks have been developed, to cite a few
Commercial Software : Statistica Neural Network, TNs2Server, DataEngine, Know Man
Basic Suite, Partek, Saxon, ECANSE - Environment for Computer Aided Neural Software
Engineering, Neuroshell, Neurogen, Matlab:Neural Network Toolbar.
Freeware Software: Net II, Spider Nets Neural Network Library, NeuDC, Binary Hopfeild
Net with free Java source, Neural shell, PlaNet, Valentino Computational Neuroscience Work
bench, Neural Simulation language version-NSL, Brain neural network Simulator.
Conclusion
The computing world has a lot to gain from neural networks. Their ability to learn by example
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8. Artificial Neural Networks and its Applications
makes them very flexible and powerful. A large number of claims have been made about the
modeling capabilities of neural networks, some exaggerated and some justified. Hence, to best
utilize ANNs for different problems, it is essential to understand the potential as well as
limitations of neural networks. For some tasks, neural networks will never replace
conventional methods, but for a growing list of applications, the neural architecture will
provide either an alternative or a complement to these existing techniques. Finally, I would
like to state that even though neural networks have a huge potential we will only get the best
of them when they are integrated with Artificial Intelligence, Fuzzy Logic and related
subjects.
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