PTCCS364 Syllabus - Soft Computing - 2023 Regulation Anna University

PTCCS364 Syllabus - Soft Computing - 2023 Regulation Anna University

PTCCS364

SOFT COMPUTING

 L T P C

2 0 2 3

COURSE OBJECTIVES:
• To introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience.
• To provide the mathematical background for carrying out the optimization associated with neural network learning
• To learn various evolutionary Algorithms.
• To become familiar with neural networks that can learn from available examples and generalize to form appropriate rules for inference systems.
• To introduce case studies utilizing the above and illustrate the Intelligent behavior of programs based on soft computing

UNIT I

INTRODUCTION TO SOFT COMPUTING AND FUZZY LOGIC

6

Introduction - Fuzzy Logic - Fuzzy Sets, Fuzzy Membership Functions, Operations on Fuzzy Sets, Fuzzy Relations, Operations on Fuzzy Relations, Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems

UNIT II

NEURAL NETWORKS

6

Supervised Learning Neural Networks – Perceptrons - Backpropagation -Multilayer Perceptrons – Unsupervised Learning Neural Networks – Kohonen Self-Organizing Networks

UNIT III

GENETIC ALGORITHMS

6

Chromosome Encoding Schemes -Population initialization and selection methods - Evaluation function - Genetic operators- Cross over – Mutation - Fitness Function – Maximizing function

UNIT IV

NEURO FUZZY MODELING

6

ANFIS architecture – hybrid learning – ANFIS as universal approximator – Coactive Neuro fuzzy modeling – Framework – Neuron functions for adaptive networks – Neuro fuzzy spectrum - Analysis of Adaptive Learning Capability

UNIT V

APPLICATIONS

6

Modeling a two input sine function - Printed Character Recognition – Fuzzy filtered neural networks –Plasma Spectrum Analysis – Hand written neural recognition - Soft Computing for Color Recipe Prediction.

30 PERIODS

COURSE OUTCOMES:
CO1: Understand the fundamentals of fuzzy logic operators and inference mechanisms
CO2: Understand neural network architecture for AI applications such as classification and clustering
CO3: Learn the functionality of Genetic Algorithms in Optimization problems
CO4: Use hybrid techniques involving Neural networks and Fuzzy logic
CO5: Apply soft computing techniques in real world applications

PRACTICAL EXERCISES: 30 PERIODS
1. Implementation of fuzzy control/ inference system
2. Programming exercise on classification with a discrete perceptron
3. Implementation of XOR with backpropagation algorithm
4. Implementation of self organizing maps for a specific application
5. Programming exercises on maximizing a function using Genetic algorithm
6. Implementation of two input sine function
7. Implementation of three input non linear function

TOTAL:60 PERIODS

TEXT BOOKS:
1. SaJANG, J.-S. R., SUN, C.-T., & MIZUTANI, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ, Prentice Hall,1997
2. Himanshu Singh, Yunis Ahmad Lone, Deep Neuro-Fuzzy Systems with Python
3. With Case Studies and Applications from the Industry, Apress, 2020

REFERENCES:
1. roj Kaushik and Sunita Tiwari, Soft Computing-Fundamentals Techniques and Applications, 1st Edition, McGraw Hill, 2018.
2. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.
3. Samir Roy, Udit Chakraborthy, Introduction to Soft Computing, Neuro Fuzzy and Genetic Algorithms, Pearson Education, 2013.
4. S.N. Sivanandam, S.N. Deepa, Principles of Soft Computing, Third Edition, Wiley India Pvt Ltd, 2019.
5. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP Professional, Boston, 1996

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