Artificial Neuron Network. (1)

8/2/2019 Artificial Neuron Network. (1)

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ArtificialNeuron

Network


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An idea

Since ancient times. ; this field wasestablished before the advent of

computers, The first artificial neuron was

produced in 1943 by theneurophysiologist Warren McCullochand the logician Walter Pits

But not realm


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Conventional computing versus

artificial neural networks

Traditional computers: processing is sequential, function logically with a set of rules and calculations, must learn only by doing different sequences or steps in an

algorithm, top-down learning.

An ANN: is an inherently multiprocessor,

can function via images, pictures, and concepts, neural networks can program themselves, Bottom-up learning.


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Getting familiar to ANN


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Model of ANN


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Rules for the operation of

the neurons1. Propagation delay is assumed to be constant for all

neurons,

2. Neurons fire at discrete moments, not continuously.

3. Each synapse output stage impinges onto only onesynaptic input stage on a subsequent neuron.

4. Each neuron can have a number of input synapticstages.

5. Synaptic input stages contribute to overcoming of athreshold below which the neuron will not fire.


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Firing rules

X1: 0 0 0 0 1 1 1 1

X2: 0 0 1 1 0 0 1 1

X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0/1 0/1 0/1 1 0/1 1


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The perceptron

The perceptron is a mathematical model of a

biological neuron.


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a perceptron calculates the weighted sum of the inputvalues.

the perceptron outputs a non-zero value only whenthe weighted sum exceeds a certain threshold.

Output of P = {1 if A x + B y > C

{0 if A x + B y < = C


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Architecture of Neural

Networks

Competitive neural networks

Feed-forward networks

Feed-back networks


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Simple competitive

networks:


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Composed of

The Hemming net The Maxnet


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Each perceptron at the top layer of the Hemmingnet calculates a weighted sum of the input values.This weighted sum can be interpreted as the dot

product of the input vector and the weight vector.

The maxnet is a fully connected network with eachnode connecting to every other nodes, including

itself. The basic idea is that the nodes competeagainst each other by sending out inhibiting signals

to each other.


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In a simple competitive network, a Maxnetconnects the top nodes of the Hemming net.

Whenever an input is presented, the Hemming netfinds out the distance of the weight vector ofeach node from the input vector via the dotproduct, while the Maxnet selects the node with thegreatest dot product. In this way, the wholenetwork selects the node with its weight vectorclosest to the input vector, i.e. the winner.


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Feed-Forward networks


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Feed-forward networks

characteristics 1. Perceptrons are arranged in layers, with the first

layer taking in inputs and the last layer producingoutputs. The middle layers have no connection withthe external world, and hence are called hiddenlayers.

2. Each perceptron in one layer is connected toevery perceptron on the next layer. Henceinformation is constantly "fed forward" from onelayer to the next., and this explains why thesenetworks are called feed-forward networks.

3. There is no connection among perceptrons in thesame layer.


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feed-forward networks are commonly used for

classification.


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Back-propagation -- learning in

feed-forward networks

pairs of input and output values are fed into thenetwork for many cycles, so that the network'learns' the relationship between the input andoutput.

{i = (1, 2) , o =( 0, 0)i = (1, 3) , o = (0, 0)i = (2, 3) , o = (1, 0)i = (3, 4) , o = (1, 0)

i = (5, 6) , o = (0, 1)i = (6, 7) , o = (0, 1)

}


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BACK-PROPAGATIONFORMULAE


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Feedback networks


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The Learning Process

Associative mapping

Auto-association

Hetero-association

Regularity detection


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Supervised learning

Unsupervised learning


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Transfer/ActivationFunction

The Activation Function accepts a value that is theweighted sum of neuron inputs and returns a valuethat represents the output of the neuron.

This function should be used for training neuralnetworks because a continuous function like thisgives better feedback about the degree of error in anetwork.

To make a neural network that performs somespecific task, we must set the weights on theconnections appropriately.


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Sigmoid Function


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Some specific details of

neural networks

Classification. (pattern recognition

programs ) Prediction. (stock market prediction)

Clustering. (data-mining )

Association. ("remember" )


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Applications of neuralnetworks

Sales forecasting. Industrial process control. Data validation. Target marketing. Recognition of speakers in

communications. Diagnosis of hepatitis. Recovery of telecommunications from

faulty software. Facial recognition


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Conclusion

The computing world has a lot to gainfrom neural networks. Neural networks

have a huge potential we will only getthe best of them when they areintegrated with computing, AI, fuzzy

logic and related subjects.

Artificial Neuron Network. (1)

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