MP4093 Soft Computing Techniques Syllabus:
MP4093 Soft Computing Techniques Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To give the knowledge of soft computing theories fundamentals
To provide the mathematical background for carrying out the optimization associated with neural network learning
To familiarize the ideas of fuzzy sets, fuzzy logic, use of heuristics and Fuzzy Logic Control Systems
To introduce the mathematical background for genetic algorithms
To expose the hybrid soft computing systems and its applications
UNIT I SOFT COMPUTING FUNDAMENTALS
Introduction: Soft Computing Constituents – From Conventional Al to Computational Intelligence – Applications – Introduction, characteristics- learning methods – taxonomy – Evolution of neural networks – Artificial Neural Network (ANN): Fundamental Concept – Basic Terminologies – Neural Network Architecture – Learning Process – Fuzzy logic: Introduction – crisp – sets- fuzzy sets – crisp relations and fuzzy-relations: Cartesian product
UNIT II NEURAL NETWORKS
Fundamental Models of ANN: McCulloch- Pitts Model –Hebb Network – Linear Separability Pitts Model –Hebb Network – Supervised Learning Networks: Perceptron Network – Adaline and Madaline Networks – Back Propagation Network – Radial Basis Function Network – Unsupervised Learning Networks: Kohonen Self Organizing Network – ART network – Hopfield Network – Special Network– Support Vector Machine- Kernel methods for Pattern classification- Kernel methods for function optimization.
UNIT III FUZZY COMPUTING AND MODELING
Fuzzy Equivalence and Tolerance Relation – Value assignments- Fuzzy Composition- Membership Functions–Fuzzification- Defuzzification: lambda cuts – Fuzzy Arithmetic – Extension Principle – Fuzzy Measures –Fuzzy Classification – Fuzzy Rules and Fuzzy Reasoning: Fuzzy Propositions – Formation of Rules – Decomposition of Rules – Aggregation of Rules – Approximate Reasoning – Fuzzy Inference and Expert Systems – Fuzzy Decision Making – Fuzzy Logic Control Systems.
UNIT IV GENETIC ALGORITHM AND APPLICATIONS
Genetic Algorithm: Fundamental Concept – Basic Terminologies – Traditional Vs Genetic Algorithm – Elements of GA – Encoding – Fitness Function – Genetic Operators: Reproduction – Cross Over – Inversion and Deletion – Mutation – Simple and General GA – The Schema Theorem difference between GA and GP- Applications of GA. Multi-objective Optimization- Real-life case studies – optimization of traveling salesman problem using genetic algorithms
UNIT V HYBRID SOFT COMPUTING AND APPLICATIONS
Case Studies: Neuro-fuzzy Hybrid system- genetic neuro hybrid systems – genetic fuzzy hybrid and fuzzy genetic hybrid systems – simplified fuzzy ARTMAP – Applications: A fusion approach of multispectral images with SAR – Knowledge Leverage Based TSK Fuzzy System Modeling – Fuzzy C-Means algorithms for very large Data. Hybrid GA for Feature Selection- Multi objective Genetic Fuzzy Clustering for pixel classification- Clustering Wireless Sensor Network Using Fuzzy Logic and Genetic Algorithm
COURSE OUTCOMES:
After completion of the course, the student will be able to:
CO1: Apply various soft computing concepts for practical applications
CO2: Choose and design suitable neural network for real time problems
CO3: Use fuzzy logic rules and reasoning to handle uncertainty and develop decision making and expert system
CO4: Describe the importance of genetic algorithms for solving combinatorial optimization problems
CO5: Analysis the various hybrid soft computing techniques and apply in real time problems
TOTAL: 45 PERIODS
REFERENCES:
1. S.N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”, Wiley, Second Edition, 2011.
2. S. Rajasekaran, G.A.V Vijayalakshmi Pai, “Neural Networks, Fuzzy Systems and Evolutionary Algorithms: Synthesis and Applications” Prentice Hall, Second Edition, 2017.
3. Timothy J. Ross, “Fuzzy Logic with Engineering Applications, 4th Edition, Wiley 2016.
4. David E. Goldberg, Genetic Algorithm in Search Optimization and Machine Learning Pearson Education India, 2013.
5. Simon Haykin, Neural Networks Comprehensive Foundation Third Edition, Pearson Education, .2016.
6. James A. Freeman, David M. Skapura, Neural Networks Algorithms, Applications, and Programming Techniques, Pearson Education India, 2011.
7. J. -S. R. Jang, C.-T. Sun, E. Mizutani, “Neuro Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Pearson, 2015.