SOFT COMPUTING
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 1 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.
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
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
