Basics of Artificial Neural Network

Basics of Artificial Neural Network

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  CONCEPT OF ARTIFICIALNEURAL NETWORK Nayagarh, 752069, Odisha · Whatsapp- 9090949732 Mail- [email protected] Linked in :- www.linkedin.com/in/subham-preetam-97b2b0148 Researchgate ID:- https://www.researchgate.net/profile/Subham_Preetam2 SUBHAM PREETAM
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  CONTENTS • INTRODUCTION • BIOLOGICALNEURONMODEL •ARTIFICIALNEURONMODEL • ARTIFICIALNEURALNETWORK • NEURALNETWORKARCHITECTURE • LEARNING • BACKPROPAGATIONALGORITHM • APPLICATIONS • ADVANTAGES Dear all Here we discussing the basic benefits of neural coding system . These mentation formula we getting collected for various authors. As the very adopting technological field, artificial neural network provide for basic platform in cloud technology, artificial intelligence & Machine learning. Thanks and Regards Subham Preetam
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  INTRODUCTION • “Neural“ isan adjective for neuron, and “network” denotes agraph like structure. • Artificial Neural Networks are also referred to as“neural nets” , “artificial neural systems”, “parallel distributed processing systems”, “connectionist systems”. • For acomputing systemsto be called by these pretty names, it is necessary for the system to have alabeled directed graph structure where nodes performs some simplecomputations. • “DirectedGraph” consists of set of “nodes”(vertices) and aset of “connections”(edges/links/arcs) connecting pair of nodes. • Agraph is saidto be “labeled graph” if eachconnection is associated with a label to identify some property of the connection
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  Fig1:ANDgate graph This graphcannot be considered a neural network since the connections between the nodes are fixed and appear to play no other role than carrying the inputs to the node that computed theirconjunction. Fig2:AND gate network The graph structure which connects the weights modifiable using a learning algorithm, qualifies the computing system to be called an artificial neural networks. x2ϵ{0,1} x1 x2 x1ϵ{0,1} o = x1 AND x2 multiplier (x1 w1) (x2w2) o = x1 AND x2 x1 x2 w1 w2 •The field of neuralnetwork was pioneered by BERNARD WIDROW of Stanford University in 1950’s. CONTD…
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  BIOLOGICAL NEURON MODEL Fourpartsof a typical nervecell :- • DENDRITES:Accepts theinputs • SOMA: Process theinputs • AXON : Turns the processed inputs into outputs. • SYNAPSES: Theelectrochemical contact betweenthe neurons.
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  ARTIFICIAL NEURON MODEL •Inputs to the network are represented bythe mathematical symbol,xn • Each of these inputs are multiplied by a connection weight ,wn sum =w1 x1+……+wnxn • These products are simply summed, fed through the transfer function, f( ) to generate aresult and thenoutput. w1 w2 xn x2 x1 wn f(w1 x1+……+wnxn)
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  TERMINOLOGY Biological Terminology ArtificialNeural Network Terminology Neuron Node/Unit/Cell/Neurode Synapse Connection/Edge/Link Synaptic Efficiency Connection Strength/Weight Firing frequency Node output
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  ARTIFICIAL NEURAL NETWORK •Artificial Neural Network (ANNs) are programs designed to solve any problem by trying to mimic the structure and the function of our nervous system. • Neural networks are based on simulated neurons, Which are joined together in avariety of ways to form networks. • Neural network resembles the human brain in the following two ways:- * Aneural network acquires knowledge through learning. *A neural network’s knowledge is stored within the interconnection strengths knownassynaptic weight.
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  ARTIFICIAL NEURALNETWORK MODEL outputlayer connections Input layer Hidden layers Neural network Including connections (called weights) between neuron Com p are Actual output Desired output Input output Figure showing adjust of neural networkFig 1: artificial neural network model CONTD…
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  NEURAL NETWORKARCHITECTURES These arenetworks in which nodes are partitioned into subsetscalled layers, with no connections from layer j to k if j >k. Input node Input node output node output node Hidden node Layer3Layer0 (Input layer) (Outputlayer) Layer 1 Layer2 Hidden Layer Fig: fully connectednetwork The neural network in which every node is connected to every other nodes, and these connections may be either excitatory (positive weights), inhibitory (negative weights), or irrelevant (almost zero weights). fig: layered network
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  This is thesubclass of the layered networks in which there is no intra-layer connections. In other words, a connection may exist between any node in layer i and any node in layer j for i <j, but aconnection is not allowed for i=j. This is a subclass of acyclic networks in which aconnection is allowed from anode in layer i only to nodes in layer i+1 Layer0 (Input layer) Layer3 (Outputlayer) Layer 1 Layer2 Hidden Layer Layer 1 Layer2 Layer3Layer0 (Input layer) (Outputlayer) Hidden Layer fig : Feedforward networkFig :Acyclic network CONTD…
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  Many problems arebest solved using neural networks whose architecture consists of several modules, with sparse interconnections between them. Modules can be organized in several different ways asHierarchial organization, Successive refinement, Inputmodularity FIG : MODULAR NEURAL NETWORK CONTD…
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  LEARNING • Neurons inan animal’s brain are “hard wired”. It is equally obvious that animals, especially higher order animals, learn asthey grow. • How does thislearning occur? • What arepossible mathematical models of learning? • In artificial neural networks, learning refers to the method of modifying the weights of connections between the nodesof a specified network. • The learning ability of aneural network is determined by its architecture and by the algorithmic method chosen for training.
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  • This islearning bydoing. • In this approach no sample outputs are provided to the network against which it can measure its predictive performancefor agiven vector of inputs. • One common form of unsupervised learning is clustering where we try to categorize data indifferent clusters by theirsimilarity. UNSUPERVISED LEARNING • A teacher is available to indicate whether asystem is performing correctly, or to indicate the amount of error in system performance. Here a teacher is aset of training data. • The training data consist of pairs of input and desired output values that aretraditionally represented in data vectors. • Supervised learning can also be referred asclassification, wherewe have awide range of classifiers, (Multilayer perceptron, k nearest neighbor..etc) SUPERVISED LEARNING CONTD…
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  THE BACKPROPAGATIONALGORITHM • Thebackpropagationalgorithm (Rumelhart and McClelland, 1986) is usedin layered feed-forwardArtificial Neural Networks. • Backpropagation is amulti-layer feed forward, supervised learning network based on gradient descent learning rule. • we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error (difference between actual and expected results) is calculated. • Theidea of the backpropagation algorithm is to reducethis error, until theArtificial Neural Network learns the training data.
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  • The activationfunction of the artificial neurons in ANNs implementing the backpropagation algorithm is a weighted sum(the sumof the inputs xi multiplied by their respective weights wji) • Themost common output function is the sigmoidal function: • Since the error is the difference between the actual and the desired output, the error depends on the weights, and we need to adjust the weights in order to minimize the error. Wecan define the error function for the output of eachneuron: Inputs, x Weights,v weights, w output Fig: Basic Blockof Back propagation neuralnetwork
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  Weights,v weights, w CONTD… •If we want to adjust vik, the weights (let’s call them vik ) of aprevious layer, we need first to calculate how the error depends not on the weight, but in the input from the previous layer i.e. replacing w by x asshown in belowequation. Inputs, x output where • and
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  ADVANTAGES • Itinvolves humanlike thinking. • They handle noisy or missing data. • They canwork with large number of variables or parameters. • They provide general solutions with good predictive accuracy. • System hasgot property of continuous learning. • They deal with the non-linearity in the world in which we live.
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  THANK YOU