Aravali College of Engineering and Management, Faridabad

1. Program Name : B.Tech CSECourse Name: Machine LearningNEURAL NETWORKS

2. CONTENTS • Introduction. • Artificial NeuralNetworks. • Model of ArtificialNeurons. • Neural NetworkArchitecture. • Single Layer Feed ForwardNetworks. • Learning ofANN. • Applications ofANN.

3. INTRODUCTION • Neuralnetworksarethesimplifiedmodelsofthe biological neuronsystems. • Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes'.which contain an 'activationfunction'. • Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers'where the actual processing is done via a system of weighted 'connections'. • The hidden layers then link to an 'output layer' wherethe answer isoutput

4. ARTIFICIAL NEURALNETWORKS Output Inputs An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

5. MODEL OF ARTIFICIALNEURON • An appropriate model/simulation of the nervous system should beable to produce similar responses and behaviours in artificialsystems. • The nervous system is build by relatively simple units, the neurons,so • copying their behaviour and functionality should be thesolution.

6. MODEL OF ARTIFICIALNEURON Neuron consists of three basic components weights, thresholds and a single activationfunction A setor connection link: each of which is characterized by a weightor strength of its own wkj. Specifically, a signal xj at the input synapse „j‟ connected to neuron „k‟ is multiplied by the synapticwkj An adder: For summing the input signals, weighted by respective synaptic strengths of the neuron in a linearoperation. I w1x1 w2x2 ....... wnxn wixi n i 1

7. MODEL OF ARTIFICIALNEURON Threshold for aNeuron:- The total input for each neuron is the sum of the weighted inputsto the neuron minus its threshold value. This is then passed through the sigmoid function. The equation for the transition in a neuron is: a = 1/(1 + exp(- x))where x = ai wi -Q a is the activation for theneuron ai is the activation for neuron i wi is theweight Q is the thresholdsubtracted

8. MODEL OF ARTIFICIALNEURON • Activation function: An activation function f performs amathematical operation on the signal output. The most common activation functionsare: • - LinearFunction, • ThresholdFunction, • Sigmoidal (S shaped)function, • The activation functions are chosen depending upon the typeof problem to be solved by thenetwork.

9. MODEL OF ARTIFICIALNEURON Activation Functions f – Types:- Sigmoidal Function (S-shapefunction):- The nonlinear curved S-shape function is called the sigmoidfunction. This is most common type of activation used to construct the neural networks. It is mathematically well behaved, differentiable andstrictly increasingfunction. 1 Y f(I) ,0 f(I) 1 1 e 1/(1 exp( I)),0 f(I) 1 I This is explainedas ≈ 0 for large -ve inputvalues, 1 for large +ve values,witha smooth transition between thetwo. α is slope parameter also called shape parameter symbol the λ isalso usedto represented thisparameter.

10. NEURAL NETWORKARCHITECTURE • An artificial Neural Network isdefinedas a data processing system consisting of a large numberofinterconnected processing elementsor artificialneurons. • There are three fundamentally differentclassesof neuralnetworks. Thoseare. • Single layerfeedforward Networks. • Multilayer feedforwardNetworks. • Recurrent Networks. • Here we have to discuss the single layer feed forwardnetwork.

11. 20 March2013 SINGLE-LAYER FEED FORWARDNETWORK • Input layer of source nodes that projectsdirectly • onto an output layer ofneurons. • “Single-layer” referring to the output layer of computation nodes (neuron).

12. SINGLE-LAYER FEED FORWARDNETWORK Ii1 Ii2 Oi1 W11 1 Yo1 Io1 W21 1 Oi2 2 Yo2 Io2 Ii3 2 Oi3 W31 3 Yo m Iom Iin Wn1 3 Oin 4 • The above figure is a single layer feed forward neural network. Itconsists an input layer to receive the inputs and an output layer to output the vectors. • The input layer consists of „n‟ neurons, andtheoutput layer contains „m‟ • neurons. • The weight of synapse connecting ith input neuron the jth outputneuron isWij.

13. SINGLE-LAYER FEED FORWARDNETWORK Here the inputs of the input layer and the outputs of the output layeris givenas Oo1 Oo2 .. Ii1 Ii2 .. O I o 1 Oom ...... Iin m1 n1 W2 jII2 WnjIIN So Ioj W1jII1 Hence,theinput to the output layer can be givenas WT WT I O I I I o m1 mn n1 OI II Because n1 m1 F(I,W) I O The block diagram of a single layer feed forwardnetwork.

14. LEARNING INANN • Learning methods in neural networks can be broadly classified in three basic types. • SupervisedLearning • UnsupervisedLearning • ReinforcementLearning • SupervisedLearning:- • In supervised learning, both the inputs and the outputs areprovided. The network then processes the inputs and compares its resulting outputs against the desiredoutputs • Errors are then calculated, causing the system to adjust theweights which control thenetwork. • Here a teacher is assume tobepresent during the learningprocess. 

15. LEARNING INANN • UnsupervisedLearning:- • Here the target output isnotpresented to the network, Becausethere • is no teacher to present the describedpatterns. • Sothesystemlearnsofits ownbydiscoveringandadapting to structuralfeaturesoftheinputpatterns. • Reinforcement Learning:- • In this method, a teacher though available, does not present the expected answerbutonly indicates if the computed output is correct orincorrect. • Theinformation provided helps the network in its learningprocess. • Here a rewardisgiven for correct answercomputed and a penalty for a wronganswer.

16. Character Recognition:- Neural networks can be used torecognize handwrittencharacters. • Image Compression:- Neural networks can receive and processvast amounts of information at once, making them useful in image compression. • Stock Market Prediction:- Neural networks can examine a lot of information quickly and sort it all out, they can be used to predictstock prices. • Travelling Salesman Problem:- Neural networks can solvethe traveling salesman problem, but only to a certain degree of approximation. • Security and Loan Applications:- With the acceptation of aneuralnetwork that will decide whether or not to grant aloan. APPLICATIONS OF NEURALNETWORKS

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