Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Machine learning by using python lesson 2 Neural Networks By Professor Lili S...Professor Lili Saghafi
When we say "Neural Networks", we mean artificial Neural Networks (ANN). The idea of ANN is based on biological neural networks like the brain.
The basic structure of a neural network is the neuron. A neuron in biology consists of three major parts: the soma (cell body), the dendrites, and the axon.
The dendrites branch of from the soma in a tree-like way and getting thinner with every branch. They receive signals (impulses) from other neurons at synapses. The axon - there is always only one - also leaves the soma and usually tend to extend for longer distances than the dentrites. The axon is used for sending the output of the neuron to other neurons or better to the synapsis of other neurons.
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This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
Machine learning by using python lesson 2 Neural Networks By Professor Lili S...Professor Lili Saghafi
When we say "Neural Networks", we mean artificial Neural Networks (ANN). The idea of ANN is based on biological neural networks like the brain.
The basic structure of a neural network is the neuron. A neuron in biology consists of three major parts: the soma (cell body), the dendrites, and the axon.
The dendrites branch of from the soma in a tree-like way and getting thinner with every branch. They receive signals (impulses) from other neurons at synapses. The axon - there is always only one - also leaves the soma and usually tend to extend for longer distances than the dentrites. The axon is used for sending the output of the neuron to other neurons or better to the synapsis of other neurons.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
2. Introduction to Neural Networks
• In any human being, the nervous system coordinates the
different actions by transmitting signals to and from different
parts of the body.
• The nervous system is constituted of a special type of cell,
called neuron or nerve cell, which has special structures
allowing it to receive or send signals to other neurons.
• Neurons connect with each other to transmit signals to or
receive signals from other neurons. This structure essentially
forms a network of neurons or a neural network.
3. Con..
• A neural network is a computational model inspired by the
structure and functioning of the human brain. It is a type of
machine learning algorithm that consists of interconnected
nodes, commonly known as neurons, organized into layers.
Each neuron receives input, processes it using an activation
function, and produces an output that is passed on to other
neurons.
Input Layer
Hidden
Layers
Output
Layer
Weights
and Biases
Activation
Function
Components
of Neural
Network
4. UNDERSTANDING THE
BIOLOGICAL NEURON
• The human nervous system has two main parts –
– the central nervous system (CNS) consisting of the brain
and spinal cord
– the peripheral nervous system consisting of nerves and
ganglia outside the brain and spinal cord.
• The CNS integrates all information, in the form of signals,
from the different parts of the body.
• Neurons are basic structural units of the CNS.
• The peripheral nervous system, connects the CNS with the
limbs and organs.
5. UNDERSTANDING THE
BIOLOGICAL NEURON
• A neuron is able to receive, process, and transmit information
in the form of chemical and electrical signals.
• Three Main Parts
1. Dendrites – to receive signals from neighboring neurons.
2. Soma – main body of the neuron which accumulates the signals
coming from the different dendrites. It ‘fires’ when a sufficient
amount of signal is accumulated.
3. Axon – last part of the neuron which receives signal from soma,
once the neuron ‘fires’, and passes it on to the neighboring
neurons through the axon terminals (to the adjacent dendrite of the
neighboring neurons).
6. UNDERSTANDING THE
BIOLOGICAL NEURON
• There is a very small gap between the axon terminal of one neuron and the
adjacent dendrite of the neighbouring neuron.
• This small gap is known as synapse.
• The signals transmitted through synapse may be excitatory or inhibitory
7. THE ARTIFICIAL NEURON
• The biological neural network has been modelled in the form of
ANN with artificial neurons simulating the function of biological
neurons
As depicted in
Figure input
signal x
(x1 , x2 , …, xn )
comes to an
artificial neuron
8. • Each neuron has three major components
1. A set of ‘i’ synapses having weight wi . A signal xi forms the
input to the i-th synapse having weight wi . The value of weight
wi may be positive or negative. A positive weight has an
excitatory effect, while a negative weight has an inhibitory effect
on the output of the summation junction, ysum
2. A summation junction for the input signals is weighted by the
respective synaptic weight. Because it is a linear combiner or
adder of the weighted input signals, the output of the summation
junction, ysum , can be expressed as follows:
THE ARTIFICIAL NEURON
9. 3. A activation function (or simply activation function, also called
squashing function) results in an output signal only when an input
signal exceeding a specific threshold value comes as an input. It is
similar in behaviour to the biological neuron which transmits the
signal only when the total input signal meets the firing threshold.
• Output of the activation function, y , can be expressed as follows:
THE ARTIFICIAL NEURON
10. Identity function
• Identity function is used as an activation function for the input
layer. It is a linear function having the form
TYPES OF ACTIVATION FUNCTIONS
• As obvious, the output remains the same as the input.
11. Step function
• Step function gives 1 as output if the input is either 0 or positive.
If the input is negative, the step function gives 0 as output.
Expressing mathematically,
TYPES OF ACTIVATION FUNCTIONS
12. Threshold function
• The threshold function is almost like the step function, with the
only difference being the fact that θ is used as a threshold value
instead of 0. Expressing mathematically,
TYPES OF ACTIVATION FUNCTIONS
14. ReLU (Rectified Linear Unit) function
ReLU is the most popularly used activation function in the areas of
convolutional neural networks and deep learning. It is of the form
TYPES OF ACTIVATION FUNCTIONS
This means that f(x) is zero when x is less than zero and f(x) is equal
to x when x is above or equal to zero
16. Sigmoid function
The need for sigmoid function stems from the fact that many
learning algorithms require the activation function to be
differentiable and hence continuous. Step function is not suitable in
those situations as it is not continuous. There are two types of
sigmoid function:
1. Binary sigmoid function
2. Bipolar sigmoid function
TYPES OF ACTIVATION FUNCTIONS
17. Binary sigmoid function
TYPES OF ACTIVATION FUNCTIONS
where k = steepness or slope parameter of the sigmoid function. By
varying the value of k, sigmoid functions with different slopes can be
obtained. It has range of (0, 1).
18. Bipolar sigmoid function
TYPES OF ACTIVATION FUNCTIONS
The range of values of sigmoid functions can be varied depending on
the application. However, the range of (−1, +1) is most commonly
adopted.
20. Hyperbolic tangent function
TYPES OF ACTIVATION FUNCTIONS
Hyperbolic tangent function is another continuous activation
function, which is bipolar in nature. It is a widely adopted activation
function for a special type of neural network known as
backpropagation network
21. • Single-layer feed forward is the simplest and most basic
architecture of ANNs. It consists of only two layers as depicted
in Figure – the input layer and the output layer.
• The input layer consists of a set of ‘m’ input neurons X1 , X2 , …,
Xm connected to each of the ‘n’ output neurons Y1 , Y2 , …, Yn .
The connections carry weights w11 , w12 , …, wmn .
• The input layer of neurons does not conduct any processing –
they pass the input signals to the output neurons. The
computations are performed only by the neurons in the output
layer.
• only one layer is performing the computation. This is the reason
why the network is known as single layer in spite of having two
layers of neurons.
Single-layer feed forward network
23. • The multi-layer feed forward network is quite similar to the
single-layer feed forward network, except for the fact that there
are one or more intermediate layers of neurons between the input
and the output layers.
• The net signal input to the neuron in the hidden layer is given by
Multi-layer feed forward ANNs
• for the k-th hidden layer neuron. The net signal input to the
neuron in the output layer is given by
25. • In the case of recurrent neural networks, there is a small
deviation. There is a feedback loop, as depicted in Figure, from
the neurons in the output layer to the input layer neurons. There
may also be self-loops.
Recurrent network
27. There are four major aspects which need to be decided:
1. The number of layers in the network
2. The direction of signal flow
3. The number of nodes in each layer
4. The value of weights attached with each interconnection between
neurons
LEARNING PROCESS IN ANN
28. Number of layers
• A neural network may have a single layer or multi-layer
• In the case of a single layer, a set of neurons in the input layer
receives signal, i.e. a single feature per neuron, from the data set.
• The value of the feature is transformed by the activation function
of the input neuron. The signals processed by the neurons in the
input layer are then forwarded to the neurons in the output layer.
• The neurons in the output layer use their own activation function
to generate the final prediction.
LEARNING PROCESS IN ANN
29. Direction of signal flow
• In certain networks, termed as feed forward networks, signal is
always fed in one direction, i.e. from the input layer towards the
output layer through the hidden layers, if there is any
• In certain networks, such as the recurrent network, also allow
signals to travel from the output layer to the input layer.
LEARNING PROCESS IN ANN
30. Number of nodes in layers
• In the case of a multi-layer network, the number of nodes in each
layer can be varied.
• the number of nodes or neurons in the input layer is equal to the
number of features of the input data set.
• the number of output nodes will depend on possible outcomes, e.g.
number of classes in the case of supervised learning.
• the number of nodes in each of the hidden layers is to be chosen by
the user
• A larger number of nodes in the hidden layer help in improving the
performance
• Too many nodes may result in overfitting as well as an increased
computational expense
LEARNING PROCESS IN ANN
31. Weight of interconnection between neurons
• we can start with a set of values for the synaptic weights and
keep doing changes to those values in multiple iterations.
• In the case of supervised learning, the objective to be pursued is
to reduce the number of misclassifications. Ideally, the iterations
for making changes in weight values should be continued till
there is no misclassification.
• Practical stopping criteria may be the rate of misclassification
less than a specific threshold value, say 1%, or the maximum
number of iterations reaches a threshold, say 25, etc.
LEARNING PROCESS IN ANN
32. • ANN is to assign the inter-neuron connection weights. It can be a
very intense work, more so for the neural networks having a high
number of hidden layers or a high number of nodes in a layer
• In 1986, an efficient method of training an ANN was discovered.
In this method, errors, i.e. difference in output values of the
output layer and the expected values, are propagated back from
the output layer to the preceding layers
• The algorithm implementing this method is known as
backpropagation.
• The backpropagation algorithm is applicable for multi-layer feed
forward networks.
BACK PROPAGATION
33. • It is a supervised learning algorithm which continues adjusting the
weights of the connected neurons with an objective to reduce the
deviation of the output signal from the target output.
• This algorithm consists of multiple iterations, also known as epochs.
• Each epoch consists of two phases –
– A forward phase in which the signals flow from the neurons in
the input layer to the neurons in the output layer through the
hidden layers. The weights of the interconnections and
activation functions are used during the flow. In the output layer,
the output signals are generated.
– A backward phase in which the output signal is compared with
the expected value. The computed errors are propagated
backwards from the output to the preceding layers. The errors
propagated back are used to adjust the interconnection weights
between the layers.
BACK PROPAGATION