CT79 Soft Computing

Introduction: Introduction to Soft Computing Concepts, Importance of tolerance in imprecision and uncertainty, Soft Computing Constituents and Conventional Artificial Intelligence, From Conventional AI to Computational Intelligence, Fuzzy Set Theory, Neural Networks and Evolutionary Computation (2 hours)

Fuzzy Sets and Fuzzy Logic: Fuzzy sets versus Crisp sets, operations on fuzzy sets, Fuzzy Sets and Fuzzy Set Operations, Multicriteria Decision Making, Fuzzy Relations and Fuzzy Inference, Fuzzy Rule-based Systems (10 hours)

Artificial Neural Network: The neuron as a simple computing element, the Perceptron, Multilayer Neural Networks, Supervised Learning Neural Networks, Unsupervised Learning Neural Networks, Radial Basis Function Networks, Reinforcement Learning, Self-Organizing Maps, Adaptive Resonance Theory, Associative Memories, Applications. (10 hours)

Evolutionary Computation: Genetic Algorithms and Genetic Programming, Evolutionary Programming, Evolutionary Strategies and Differential Evolution Coevolution, different operators of Genetic Algorithms, analysis of selection operations, convergence of Genetic Algorithms (10 hours)

Rough Sets: Introduction, Imprecise Categories Approximations and Rough Sets, Reduction of Knowledge, Decision Tables, and Applications. (6 hours)

Hybrid Systems: 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. (4 hours)

 

References:

  1. J.-S.R Jang., C.-T Sun., & E. Mizutani; Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, Prentice-Hall of India Pvt. Ltd., 2004.
  2. K.H. Lee; First Course on Fuzzy Theory and Applications, Springer, 2005
  3. George J. Klir., Yuan Bo; Fuzzy Sets and Fuzzy Logic Theory and Applications, Prentice-Hall of India Pvt. Ltd., 2001.
  4. Simon Haykin; Neural Networks- A Comprehensive Foundation, Pearson Education Asia, 2nd ed., 2001.
  5. D. E. Goldberg; Genetic Algorithms in Search, Optimization & Machine Learning, Pearson Education Asia, 2001.
  6. S. Rajasekaran, G. A. Vijaylakshmi. Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, Prentice-Hall of India Pvt. Ltd., 2003.
  7. Amit Konar; Computational Intelligence: Principles, Techniques and Applications, Springer, 2005.
  8. Andries P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley and Sons, 2007.
  9. Russell C. Eberhart and Yuhui Shi, Computational Intelligence: Concept to Implementations, Morgan Kaufmann, 2007.
  10. Z. Pawlak, Rough Sets Theoretical Aspects of Reasoning about Data, Kluwer Academic Publisher, 1991.
  11. Da Ruan, Intelligent Hybrid Systems Fuzzy Logic, Neural Networks and genetic Algorithms, Kluwer Academic Publisher, 1997.

Punam: The following template be used to define syllabus. List broad topics in structure and detailed of each topic in Detailed Contents section with numbering upto 3 levels. Also specify total number of hours for a given topic. For you I have expanded two topics. Also elaborate each topics in detail. If possible add some more topics if possible.

(reviewed by Prof Saroj Kaushik)

Structure

1.     Introduction to soft Computing

2.     Fuzzy Sets and Fuzzy Logic

3.     Artificial Neural Network

4.     Evolutionary Computation

 

 

 

 

 


Detailed Contents of the course No of Hours (42)

 

1.       Artificial Neural Network 8

1.1.    Introduction

1.2.    Neural Network Architectures

1.2.1.        Perceptrons

1.2.2.        Multilayer Feedforward Networks

1.2.3.        Back Propagation Algorithm

1.2.4.        Training Algorithms

1.3.    Recurrent Network

1.3.1.        Hopfield Network

1.3.2.        Boltzmann Machines

1.3.3.        Radial-Basis Function Networks

1.3.3.1.              RBF Architecture

1.3.3.2.              RBF network parameters

1.3.3.3.              Learning Algorithm

1.4.    Radial-Basis Function Networks

1.4.1.        RBF Architecture

1.4.2.        RBF network parameters

1.4.3.        Learning Algorithm

1.4.4.        Neural Network Applications

1.4.5.        Neural Network Applications

1.5.    Learning Neural Networks

1.5.1.        Supervised Learning Neural Networks,

1.5.2.        Unsupervised Learning Neural Networks,

1.5.3.        Reinforcement Learning,

1.5.4.        Self-Organizing Maps,

1.5.5.        Adaptive Resonance Theory,

1.5.6.        Associative Memories

1.6.    Neural Network Applications

 

2.       Evolutionary Computation 5

2.1.    Introduction

2.2.    Genetic Algorithm

2.2.1.        Biological evolutionary process

2.2.2.        Search Space

2.2.3.        Description of Genetic Algorithm

2.2.4.        Operators of GA

2.2.4.1.              Various Genetic Operators

2.2.4.2.              Encoding schemes for Chromosome

2.3.    Applications of GA

2.4.    Genetic programming Concepts

2.4.1.        Genetic programming

2.5.    Evolutionary programming

2.6.    Learning Classifier Systems

2.7.    Genetic Algorithm and Evolutionary Programming

2.8.    Swarm intelligence

2.9.    Ant Colony Paradigm

2.9.1.        Biological Ant Colony System

2.9.2.        Ant colony optimization (ACO)

2.9.3.        Simulated Ant Colony Systems

2.9.4.        Ant Intelligent Systems

2.9.5.        Applications

2.10.Particle Swarm Optimization (PSO)‏

2.10.1.    Optimization algorithm

2.10.2.    PSO parameter control

2.10.3.    Particle Swarm Intelligent Systems

2.10.4.    Applications

Text books (not more than 3)

 

Reference books