The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs were inspired by biological neural networks and how each artificial neuron works similarly to a biological neuron by receiving input from other neurons, changing its internal activation based on that input, and sending output signals to other neurons. The document also explains that backpropagation is a learning algorithm used in ANNs where the error is calculated at the output and distributed back through the network to adjust weights between neurons in order to minimize error.
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
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.
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
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.
This lecture is about NEURAL NETWORKS WITH “R”. Artificial Neural Networks (ANNs) that starting from the mechanisms regulating natural neural networks, plan to simulate human thinking. The discipline of ANN arose from the thought of mimicking the functioning of the same human brain that was trying to solve the problem. The Machine learning is a branch of AI which helps computers to program themselves based on the input data.
In this regard, Machine learning gives AI the ability to do data-based problem solving. This lecture shows applications.
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
This lecture is about NEURAL NETWORKS WITH “R”. Artificial Neural Networks (ANNs) that starting from the mechanisms regulating natural neural networks, plan to simulate human thinking. The discipline of ANN arose from the thought of mimicking the functioning of the same human brain that was trying to solve the problem. The Machine learning is a branch of AI which helps computers to program themselves based on the input data.
In this regard, Machine learning gives AI the ability to do data-based problem solving. This lecture shows applications.
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
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.
• 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.
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.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
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.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
2. Agenda
History of Artificial Neural Networks
What is an Artificial Neural Networks?
How it works?
Learning
Learning paradigms
Supervised learning
Unsupervised learning
Reinforcement learning
Applications areas
Advantages and Disadvantages
3. History of the Artificial Neural Networks
history of the ANNs stems from the 1940s, the decade of the first electronic
computer.
However, the first important step took place in 1957 when Rosenblatt
introduced the first concrete neural model, the perceptron. Rosenblatt also
took part in constructing the first successful neurocomputer, the Mark I
Perceptron. After this, the development of ANNs has proceeded as
described in Figure.
4. History of the Artificial Neural Networks
Rosenblatt's original perceptron model contained only one layer. From this,
a multi-layered model was derived in 1960. At first, the use of the multi-
layer perceptron (MLP) was complicated by the lack of a appropriate
learning algorithm.
In 1974, Werbos came to introduce a so-called backpropagation algorithm
for the three-layered perceptron network.
5. History of the Artificial Neural Networks
in 1986, The application area of the MLP networks remained rather limited
until the breakthrough when a general back propagation algorithm for a
multi-layered perceptron was introduced by Rummelhart and Mclelland.
in 1982, Hopfield brought out his idea of a neural network. Unlike the
neurons in MLP, the Hopfield network consists of only one layer whose
neurons are fully connected with each other.
6. History of the Artificial Neural Networks
Since then, new versions of the Hopfield network have been developed. The
Boltzmann machine has been influenced by both the Hopfield network and
the MLP.
7. History of the Artificial Neural Networks
in 1988, Radial Basis Function (RBF) networks were first introduced by
Broomhead & Lowe. Although the basic idea of RBF was developed 30
years ago under the name method of potential function, the work by
Broomhead & Lowe opened a new frontier in the neural network
community.
8. History of the Artificial Neural Networks
in 1982, A totally unique kind of network model is the Self-Organizing Map
(SOM) introduced by Kohonen. SOM is a certain kind of topological map
which organizes itself based on the input patterns that it is trained with. The
SOM originated from the LVQ (Learning Vector Quantization) network the
underlying idea of which was also Kohonen's in 1972.
9. History of Artificial Neural Networks
Since then, research on artificial neural networks has
remained active, leading to many new network types, as
well as hybrid algorithms and hardware for neural
information processing.
10. Artificial Neural Network
An artificial neural network consists of a pool of simple
processing units which communicate by sending signals to
each other over a large number of weighted connections.
11. Artificial Neural Network
A set of major aspects of a parallel distributed model include:
a set of processing units (cells).
a state of activation for every unit, which equivalent to the output of the
unit.
connections between the units. Generally each connection is defined by a
weight.
a propagation rule, which determines the effective input of a unit from its
external inputs.
an activation function, which determines the new level of activation based
on the effective input and the current activation.
an external input for each unit.
a method for information gathering (the learning rule).
an environment within which the system must operate, providing input
signals and _ if necessary _ error signals.
12. Computers vs. Neural Networks
“Standard” Computers Neural Networks
one CPU highly parallel processing
fast processing units slow processing units
reliable units unreliable units
static infrastructure dynamic infrastructure
13. Why Artificial Neural Networks?
There are two basic reasons why we are interested in
building artificial neural networks (ANNs):
• Technical viewpoint: Some problems such as
character recognition or the prediction of future
states of a system require massively parallel and
adaptive processing.
• Biological viewpoint: ANNs can be used to
replicate and simulate components of the human
(or animal) brain, thereby giving us insight into
natural information processing.
14. Artificial Neural Networks
• The “building blocks” of neural networks are the
neurons.
• In technical systems, we also refer to them as units or nodes.
• Basically, each neuron
receives input from many other neurons.
changes its internal state (activation) based on the current
input.
sends one output signal to many other neurons, possibly
including its input neurons (recurrent network).
15. Artificial Neural Networks
• Information is transmitted as a series of electric
impulses, so-called spikes.
• The frequency and phase of these spikes encodes the
information.
• In biological systems, one neuron can be connected to as
many as 10,000 other neurons.
• Usually, a neuron receives its information from other
neurons in a confined area, its so-called receptive field.
16. How do ANNs work?
An artificial neural network (ANN) is either a hardware
implementation or a computer program which strives to
simulate the information processing capabilities of its biological
exemplar. ANNs are typically composed of a great number of
interconnected artificial neurons. The artificial neurons are
simplified models of their biological counterparts.
ANN is a technique for solving problems by constructing software
that works like our brains.
17. How do our brains work?
The Brain is A massively parallel information processing system.
Our brains are a huge network of processing elements. A typical
brain contains a network of 10 billion neurons.
18. How do our brains work?
A processing element
Dendrites: Input
Cell body: Processor
Synaptic: Link
Axon: Output
19. How do our brains work?
A processing element
A neuron is connected to other neurons through about 10,000
synapses
20. How do our brains work?
A processing element
A neuron receives input from other neurons. Inputs are combined.
21. How do our brains work?
A processing element
Once input exceeds a critical level, the neuron discharges a spike ‐
an electrical pulse that travels from the body, down the axon, to
the next neuron(s)
22. How do our brains work?
A processing element
The axon endings almost touch the dendrites or cell body of the
next neuron.
23. How do our brains work?
A processing element
Transmission of an electrical signal from one neuron to the next is
effected by neurotransmitters.
24. How do our brains work?
A processing element
Neurotransmitters are chemicals which are released from
the first neuron and which bind to the
Second.
25. How do our brains work?
A processing element
This link is called a synapse. The strength of the signal that
reaches the next neuron depends on factors such as the amount of
neurotransmitter available.
26. How do ANNs work?
An artificial neuron is an imitation of a human neuron
27. How do ANNs work?
• Now, let us have a look at the model of an artificial neuron.
28. How do ANNs work?
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1+X2 + ….+Xm =y
. . . . . . . . . .
. .
29. How do ANNs work?
Not all inputs are equal
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1w1+X2w2 + ….+Xmwm
=y
w1
w2
wm
weights
. . . . . . . . . .
. .
. . . .
.
30. How do ANNs work?
The signal is not passed down to the
next neuron verbatim
Transfer Function
(Activation Function)
Output
x1
x2
xm
∑
y
Processing
Input
w1
w2
wm
weights
. . . . . . . . . .
. .
f(vk)
. . . .
.
31. The output is a function of the input, that is
affected by the weights, and the transfer
functions
32.
33. Artificial Neural Networks
An ANN can:
1. compute any computable function, by the appropriate
selection of the network topology and weights values.
2. learn from experience!
Specifically, by trial‐and‐error
34. Learning by trial‐and‐error
Continuous process of:
Trial:
Processing an input to produce an output (In terms of
ANN: Compute the output function of a given input)
Evaluate:
Evaluating this output by comparing the actual
output with the expected output.
Adjust:
Adjust the weights.
35.
36.
37. How it works?
Set initial values of the weights randomly.
Input: truth table of the XOR
Do
Read input (e.g. 0, and 0)
Compute an output (e.g. 0.60543)
Compare it to the expected output. (Diff= 0.60543)
Modify the weights accordingly.
Loop until a condition is met
Condition: certain number of iterations
Condition: error threshold
38. Design Issues
Initial weights (small random values ∈[‐1,1])
Transfer function (How the inputs and the weights are
combined to produce output?)
Error estimation
Weights adjusting
Number of neurons
Data representation
Size of training set
39. Transfer Functions
Linear: The output is proportional to the total
weighted input.
Threshold: The output is set at one of two values,
depending on whether the total weighted input is
greater than or less than some threshold value.
Non‐linear: The output varies continuously but not
linearly as the input changes.
40. Error Estimation
The root mean square error (RMSE) is a frequently-
used measure of the differences between values
predicted by a model or an estimator and the values
actually observed from the thing being modeled or
estimated
41. Weights Adjusting
After each iteration, weights should be adjusted to
minimize the error.
– All possible weights
– Back propagation
42. Back Propagation
Back-propagation is an example of supervised
learning is used at each layer to minimize the
error between the layer’s response and the
actual data
The error at each hidden layer is an average
of the evaluated error
Hidden layer networks are trained this way
43. Back Propagation
N is a neuron.
Nw is one of N’s inputs weights
Nout is N’s output.
Nw = Nw +Δ Nw
Δ Nw = Nout * (1‐ Nout)* NErrorFactor
NErrorFactor = NExpectedOutput – NActualOutput
This works only for the last layer, as we can know
the actual output, and the expected output.
44. Number of neurons
Many neurons:
Higher accuracy
Slower
Risk of over‐fitting
Memorizing, rather than understanding
The network will be useless with new problems.
Few neurons:
Lower accuracy
Inability to learn at all
Optimal number.
45. Data representation
Usually input/output data needs pre‐processing
Pictures
Pixel intensity
Text:
A pattern
46. Size of training set
No one‐fits‐all formula
Over fitting can occur if a “good” training set is not
chosen
What constitutes a “good” training set?
Samples must represent the general population.
Samples must contain members of each class.
Samples in each class must contain a wide range of
variations or noise effect.
The size of the training set is related to the number of
hidden neurons
48. Supervised learning
This is what we have seen so far!
A network is fed with a set of training samples
(inputs and corresponding output), and it uses
these samples to learn the general relationship
between the inputs and the outputs.
This relationship is represented by the values of
the weights of the trained network.
49. Unsupervised learning
No desired output is associated with the
training data!
Faster than supervised learning
Used to find out structures within data:
Clustering
Compression
50. Reinforcement learning
Like supervised learning, but:
Weights adjusting is not directly related to the error
value.
The error value is used to randomly, shuffle weights!
Relatively slow learning due to ‘randomness’.
51. Applications Areas
Function approximation
including time series prediction and modeling.
Classification
including patterns and sequences recognition, novelty
detection and sequential decision making.
(radar systems, face identification, handwritten text recognition)
Data processing
including filtering, clustering blinds source separation and
compression.
(data mining, e-mail Spam filtering)
52. Advantages / Disadvantages
Advantages
Adapt to unknown situations
Powerful, it can model complex functions.
Ease of use, learns by example, and very little user
domain‐specific expertise needed
Disadvantages
Forgets
Not exact
Large complexity of the network structure
53. Conclusion
Artificial Neural Networks are an imitation of the biological
neural networks, but much simpler ones.
The computing would have a lot to gain from neural networks.
Their ability to learn by example makes them very flexible and
powerful furthermore there is need to device an algorithm in
order to perform a specific task.
54. Conclusion
Neural networks also contributes to area of research such a
neurology and psychology. They are regularly used to model
parts of living organizations and to investigate the internal
mechanisms of the brain.
Many factors affect the performance of ANNs, such as the
transfer functions, size of training sample, network topology,
weights adjusting algorithm, …
55. References
Craig Heller, and David Sadava, Life: The Science of Biology, fifth edition,
Sinauer Associates, INC, USA, 1998.
Introduction to Artificial Neural Networks, Nicolas Galoppo von Borries
Tom M. Mitchell, Machine Learning, WCB McGraw-Hill, Boston, 1997.
57. Q. How does each neuron work in ANNS?
What is back propagation?
A neuron: receives input from many other neurons;
changes its internal state (activation) based on the
current input;
sends one output signal to many other neurons, possibly
including its input neurons (ANN is recurrent network).
Back-propagation is a type of supervised learning,
used at each layer to minimize the error between
the layer’s response and the actual data.