Soft Computing Class1

7/24/2019 Soft Computing Class1

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Soft computing

BY:

ARUN PRASAD K.M.

Assistant Professor

Model Engineering College



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Soft computing techniques

Artificial Neural Networks (ANN)

Fuzzy Logic

Genetic Algorithms



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Reference Books

• S.N. Shivnandam, “Principle of soft computing”,

Wiley.

• S. Rajshekaran and G.A.V. Pai, “Neural Network

, Fuzzy logic And Genetic Algorithm”, PHI. • Jack M. Zurada, “Introduction to Artificial Neural

Network System” JAico Publication.

• Simon Haykins, “Neural Network- AComprehensive Foudation”

• Timothy J.Ross, “Fuzzy logic with Engineering

Applications”, McGraw-Hills 1.



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Artificial neural network

Human brain is a highly complex, nonlinear,

and parallel information processing system

Human Brain is made up of cells called Neurons



Syllabus

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Module 1

Introduction to Neural Network: Concept,

biological neural network, evolution of artificialneural network, McCulloch-Pitts neuron models,

Learning (Supervise & Unsupervise) and activation

function, Models of ANN-Feed forward network and

feed back network, Learning Rules- Hebbian,

Delta, Perceptron Learning and Windrow-Hoff,

winner take all.



Human brain contains

a massively

interconnected net of

10 10 -10 11 (10 billion)

neurons (cortical

cells)

Biological

Neuron

- The simple

“arithmetic

computing”

element

Brain Computer: What is it?

6



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BIOLOGICAL NEURON



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BIOLOGICAL NEURON



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BIOLOGICAL NEURON

• In the human brain, a typical neuron collectssignals from others through a host of finestructures called dendrites.

• The neuron sends out spikes of electrical

activity through a long, thin strand known asan axon, which splits into thousands ofbranches.

• At the end of each branch, a structure called

a synapse converts the activity from theaxon into electrical effects that inhibit orexcite activity from the axon into electricaleffects in the connected neurons.



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The Artificial Neuron model



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The analogy between HUMAN

NEURON and the ANN

Human Artificial

Neuron Processing Element

Dendrites Combining Function

Cell Body Transfer Function

Axons Element Output

Synapses Weights



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Activation Functions

1. Threshold

2. Signum

3. Sigmoid4. Hyperbolic tangent (tanh)

5. Piece-wise linear



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1.Threshold function



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2.Signum Function



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3.Sigmoid Function



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4.Hyperbolic Tangent



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5.Piece wise linear



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Applications of Artificial

Neural Networks

• Speech Recognition• Pattern Recognition

• Signal processing

• Text to speech Conversion• Robotics

• Control

• Medical diagnosis

• Stock Market Prediction

• Sales Forecasting

• Energy Demand Forecasting

• Parameter Estimation



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Mc Culloch and Pitts Model

For Mc-Culloch & Pitts Model, the activation function is

Threshold or signum



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Documents

Soft Computing Class1