Soft Computing (IT-8002)
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RGPV notes CBGS Bachelor of engineering
Syllabus
UNIT 1:
Introduction to Neural Network: Concept, biological neural network, evolution of artificial
neural network, McCulloch-Pitts neuron models, Learning (Supervise & Unsupervise) and
activation function, Models of ANN-Feed forward network and feed back network, Learning RulesHebbian, Delta, Perceptron Learning and Windrow-Hoff, winner take all.
UNIT 2:
Supervised Learning: Perceptron learning,- Single layer/multilayer, linear Separability,
Adaline, Madaline, Back propagation network, RBFN. Application of Neural network in forecasting,
data compression and image compression.
UNIT 3:
Unsupervised learning: Kohonen SOM (Theory, Architecture, Flow Chart, Training
Algorithm) Counter Propagation (Theory , Full Counter Propagation NET and Forward only counter
propagation net), ART (Theory, ART1, ART2). Application of Neural networks in pattern and face
recognition, intrusion detection, robotic vision.
UNIT 4:
Fuzzy Set: Basic Definition and Terminology, Set-theoretic Operations, Member Function,
Formulation and Parameterization, Fuzzy rules and fuzzy Reasoning, Extension Principal and
Fuzzy Relations, Fuzzy if-then Rules, Fuzzy Inference Systems. Hybrid system including neuro
fuzzy hybrid, neuro genetic hybrid and fuzzy genetic hybrid, fuzzy logic controlled GA. Application
of Fuzzy logic in solving engineering problems.
UNIT 5:
Genetic Algorithm: Introduction to GA, Simple Genetic Algorithm, terminology and
operators of GA (individual, gene, fitness, population, data structure, encoding, selection,
crossover, mutation, convergence criteria). Reasons for working of GA and Schema theorem, GA
optimization problems including JSPP (Job shop scheduling problem), TSP (Travelling salesman
problem), Network design routing, timetabling problem.
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Books Recommended
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.
Pearson Prentice. Hall, 2nd Edition..Simon Haykins, “Neural Network- A Comprehensive Foudation”
Timothy J.Ross, “Fuzzy logic with Engineering Applications”, McGraw-Hills 1.
Randy L. Haupt
Sue Ellen Haupt Practical Genetic Algorithms , John Wiley & Sons, , Second Edition
List of Experiment:-
Form a perceptron net for basic logic gates with binary input and output.
Using Adaline net, generate XOR function with bipolar inputs and targets.
Calculation of new weights for a Back propagation network, given the values of input
pattern, output pattern, target output, learning rate and activationfunction.
Construction of Radial Basis Function Network.
Use of Hebb rule to store vector in auto associative neural net.
Use of ART algorithm to cluster vectors.
Design fuzzy inference system for a given problem.
Maximize the function y =3x2 + 2 for some given values of x using Genetic algorithm.
Implement Travelling salesman problem using Genetic Algorithm.
Optimisation of problem like Job shop scheduling using Genetic algorithm.
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