DS4012 Soft Computing and its Applications for Signal Processing Syllabus:
DS4012 Soft Computing and its Applications for Signal Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To learn various Soft computing frameworks.
To understand the concept of fuzzy set and fuzzy logic.
To familiarize with the design of various artificial neural networks.
To gain insight onto stochastic techniques.
To gain knowledge in rough set and hybrid systems.
To understand the various optimization techniques.
UNIT I FUZZY SETS AND FUZZY LOGIC
Soft Computing: Introduction, requirement, different tools and techniques, Fuzzy sets versus crisp sets, Operations on fuzzy sets, Extension principle, Fuzzy relations and relation equations, Fuzzy numbers, Linguistic variables, Fuzzy logic, fuzzy logic controllers
UNIT II ARTIFICIAL NEURAL NETWORK
Introduction, basic models, Hebb’s learning, ADALINE, Perceptron, Multilayer feed forward network, Back propagation, Different issues regarding convergence of Multilayer Perceptron, Competitive learning, Self – Organizing Feature Maps, Adaptive Resonance Theory, Associative Memories, Applications.
UNIT III EVOLUTIONARY AND STOCHASTIC TECHNIQUES
Genetic Algorithm (GA), different operators of GA, analysis of selection operations, Hypothesis of building blocks, Schema theorem and convergence of Genetic Algorithm, Simulated annealing and Stochastic models, Boltzmann Machine, Applications.
UNIT IV ROUGH SET AND HYBRID SYSTEMS
Introduction, Imprecise Categories Approximations and Rough Sets, Decision Tables and Applications. Neural Network – based Fuzzy Systems, Fuzzy Logic – Based Neural Networks, Genetic Algorithm for Neural Network Design and Learning, Fuzzy Logic and Genetic Algorithm for Optimization, Applications.
UNIT V OPTIMIZATION TECHNIQUES
Introduction to optimization techniques, Statement of an optimization problem, classification, Unconstrained optimization-gradient search method-Gradient of a function, steepest descent, Newton’s Method, Marquardt Method, Constrained optimization –sequential linear programming, Interior penalty function method, external penalty function method.
COURSE OUTCOMES:
CO1: Develop a Fuzzy expert system.
CO2: Implement machine learning through artificial Neural networks
CO3: Develop aGenetic Algorithm (GA) for different operators
CO4: Model hybrid systems signal processing.
CO5:Able to use the optimization techniques to solve the real world problems
TOTAL:45 PERIODS
REFERENCES:
1. Neural Fuzzy Systems, Chin – Teng Lin & C. S. George Lee, Prentice Hall ,2000.
2. Fuzzy Sets and Fuzzy Logic: Theory and A: Theory and Applications , Klir & Yuan, PHI, 2015.
3. Neural Networks, S. Haykin, Pearson Education, 2ed, 2001.
4. Genetic Algorithms in Search and Optimization, and Machine Learning, D. E. Goldberg, Addison – Wesley, 1989.
5. Neural Networks, Fuzzy logic, and Genetic Algorithms, S. Rajasekaran & G. A. Vijayalakshmi Pai, PHI, 2011.
6. Neuro – Fuzzy and Soft Computing, Jang, Sun, and Mizutani, Prentice Hall,1997
7. Learning and SoftComputing, V. Kecman, MIT Press, 2001.
8. Rough Sets, Z. Pawlak, Kluwer Academic Publisher, 1991.
9. Intelligent Hybrid Systems, DaRuan, Kluwer Academic Publisher, 1997.
10. Venkata Rao and Vimal J. Savsani, Mechanical Design Optimization Using Advanced Optimization Techniques, springer 2012