IT 8th sem Soft Computing Syllabus IT802 Soft Computing Syllabus

RGTU/RGPV IT 802 Soft Computing Syllabus
Information Technology IT 8th Semester Syllabus

Unit I: 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 Rules- Hebbian, Delta, Perceptron Learning  and Windrow-Hoff, winner take all.

Unit II: 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 III: 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 IV: 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 V: 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. GA implementation using MATLAB.

IT 830 Soft Computing References:-
  •  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- A Comprehensive Foudation”   
  • Timothy J.Ross, “Fuzzy logic with Engineering Applications”, McGraw-Hills 1.
IT 830 Soft Computing 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 activation function.   
  • 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 =3x**2+ 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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