Soft Computing- Fundamentals, Techniques and Applications

1.Introduction to Soft Computing

2. Crisp and Fuzzy Sets

3. Fuzzy Logic and Inference Rules

4. Fuzzy Inference Systems

5. Rough Set and Possibility Theory

6. Single-layer Feed-forward Neural Network—Perceptron

7. Multi-layer Feed-forward Neural Network

8. Radial Basis Function Neural Network

9. Recurrent Neural Networks

10. Hybrid Intelligent System: Neuro-fuzzy Systems

11. Introduction to Evolutionary Computing

12. Genetic Algorithm Processes

13. Introduction to Swarm Intelligence

14. Introduction to Machine Learning

15. Advanced Machine-learning Techniques

Appendix A: Introduction to MATLAB

Appendix B: MATLAB Implementation of Neural Network and

Its Applications

Appendix C: MATLAB Implementation of Genetic Algorithms

Appendix D: MATLAB Implementation of Ant ColonyOptimisation

Overview

This is a comprehensive textbook on fundamentals ofmethodologies and practices in soft computing domain for students of undergraduateand postgraduate engineering and allied courses who have opted for this course.Experts on the subject have deftly explained the concepts with help of examplesand pseudo algorithms for various methods.        

Since computational intelligence and machine intelligenceare backbone and foundation for smart systems, soft computing provides basisfor building such systems. This book will equip readers to provide softcomputing techniques with low cost and reasonably good solutions to hardproblems

Key Features

Comprehensive textbook with focus on fundamentals

Topics, such as, applications of fuzzy inference system,classifiers, genetic algorithms and machine learning included

Use of MATLAB to make readers understand real applicationsof the topics included in the book 

Concept explanation with help of examples and pseudoalgorithms

Reader-centric approach

More than 150 practice questions