Artificial neural networks and its applications

What You Will Learn From This Presentation:  What is Neural Network?  What is Artificial Network Network?  What id Difference Between AAN and BNN?  How Does ANN Work?  How is the AAN Architecture?  What Are Types Of AAN?  What Are The Applications of AAN?

What Is Neural Network?  NeuralNeuron  Basic Functional Unit

What Is Neural Network?(Contd.)  Parts Of Neuron:  Dendrite-receive signal from neurons  Soma-sums up incoming signals  Axon-fires signal down the neurons  Synapse-interconnection-one neuron to other

What is Artificial Neural Network?  Biologically inspired Simulations  Used for:  Clustering  Classification  Pattern Recognition

Difference Between AAN and BNN Parameters BNN ANN Speed Fast NanoSecond s Slow Milliseconds Processing Serial Parallel Size And Complexity Less Size And Complexity More Size And Complexity Storage Replacable Complexe Interconnec

How Is ANN Developed?  Parallel Processing  Learn By Pattern  Essence Program  Use of application-specific multi-chips.

Layers Of ANN  Input Layer-receive input-outside world  Output Layer-respond to information-how it learned a task  Hidden layer-main layer-all processing-transform input- output

How Is ANN Developed?(Contd.)  Activation function-transfer function-output  Activation:  Linear  Non-Linear

How Does ANN Actually Work?  Large unit work-process information  Weighted Directed Graph  Nodes,directed edges with weights  Information-external world  Input Multiplied By Corresponding weights  Weight input-sum=1  If(sum!=1),basied added

How Does ANN Actually Work?(Contd.)  Adjusting weights and bias  Neural Network-trained first  Training-defined set of rules-learning algorithm

Neural Network Architecture  Perceptron Model in Neural Networks  Radial Basis Function Neural Network  Multilayer Perceptron Neural Network  Recurrent Neural Network  Long Short-Term Memory Neural Network (LSTM)  Hopfield Network  Convolutional Neural Network  Modular Neural Network  Physical Neural Network

Types Of Neural Network Parameters Types Based On Connection Pattern Feed Forward Recurrent Based On No.of Hidden Layer Single Layer Multilayer Based on Nature of Weight Fixed Adaptive

What Are Types Of ANN?  Classification Neural Network  Prediction Neural Network  Clustering Neural Network  Association Neural Network

Application Of ANN  Process modeling and control  Machine Diagnostics  Portfolio Management  Target Recognition  Medical Diagnosis  Credit Rating  Targeted Marketing  Voice recognition  Face recognition  Financial Forecasting  Intelligent searching  Fraud detection

Advantages Of ANN  Perform tasks-linear function/program cannot  Continue parallel processing-even if somethings fail  Implementated-any application  Doesnot need to be reprogrammed

Limitations of ANN  Needs training to be performed  Needs to be emulated  Require-high processing time

Conclusion:  ANN-very benefial  Very efficient-neural building-image recogntion  Requires efficient computing machines

At the end of this PPT ,we haved learned:  What is ANN?  How is it Developed?  What are its Types?  What its Apllications?

Artificial neural networks and its applications

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