VL4092 Soft Computing and Optimization Techniques Syllabus:
VL4092 Soft Computing and Optimization Techniques Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVE:
To classify various soft computing frame works.
To be familiar with the design of neural networks, fuzzy logic, and fuzzy systems.
To learn mathematical background for optimized genetic programming.
Be exposed to neuro-fuzzy hybrid systems and its applications.
To understand the various evolutionary optimization techniques.
UNIT I FUZZY LOGIC:
Introduction to Fuzzy logic – Fuzzy sets and membership functions- Operations on Fuzzy sets Fuzzy relations, rules, propositions, implications, and inferences- Defuzzification techniques- Fuzzy logic controller design- Some applications of Fuzzy logic.
UNIT II ARTIFICIAL NEURAL NETWORKS:
Supervised Learning: Introduction and how brain works, Neuron as a simple computing element, The perceptron, Backpropagation networks: architecture, multilayer perceptron, backpropagation learning-input layer, accelerated learning in multilayer perceptron, The Hopfield network, Bidirectional associative memories (BAM), RBF Neural Network. Unsupervised Learning: Hebbian Learning, Generalized Hebbian learning algorithm, Competitive learning, Self- Organizing Computational Maps: Kohonen Network.
UNIT III GENETIC ALGORITHM:
Genetic algorithm- Introduction – biological background – traditional optimization and search techniques – Genetic basic concepts – operators – Encoding scheme – Fitness evaluation – crossover – mutation – Travelling Salesman Problem, Particle swam optimization, Ant colony optimization.
UNIT IV NEURO-FUZZY MODELING
Adaptive Neuro-Fuzzy Inference Systems (ANFIS) – architecture – Coactive Neuro-Fuzzy Modeling, framework, neuron functions for adaptive networks – Data Clustering Algorithms – Rule base Structure Identification –Neuro-Fuzzy Control – the inverted pendulum system.
UNIT V CONVENTIONAL OPTIMIZATION TECHNIQUES
Introduction to optimization techniques, Statement of an optimization problem, classification, Unconstrained optimization-gradient search method-Gradient of a function, steepest gradient conjugate gradient, Newton’s Method, Marquardt Method, Constrained optimization –sequential linear programming, Interior penalty function method, external penalty function method.
TOTAL :45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to:
CO1:Develop application on different soft computing techniques like Fuzzy, GA and Neural network
CO2:Implement Neuro-Fuzzy and Neuro-Fuzz-GA expert system.
CO3:Implement machine learning through Neural networks.
CO4:Model Neuro Fuzzy system for clustering and classification.
CO5:Able to use the optimization techniques to solve the real world problems
REFERENCES:
1. J.S.R.Jang, C.T. Sun and E.Mizutani, Neuro-Fuzzy and Soft Computing, PHI / Pearson
2. Education 2004.
3. David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison wesley, 2009.
4. George J. Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic-Theory and Applications,Prentice Hall, 1995.
5. James A. Freeman and David M. Skapura, Neural Networks Algorithms, Applications, and Programming Techniques, Pearson Edn., 2003.
6. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall of India, 2003.
7. Mitchell Melanie, An Introduction to Genetic Algorithm, Prentice Hall, 1998.
8. Simon Haykins, Neural Networks: A Comprehensive Foundation, Prentice Hall International Inc, 1999.
9. Timothy J.Ross, Fuzzy Logic with Engineering Applications, McGraw-Hill, 1997.