CS2053 SOFT COMPUTING Syllabus

CS2053   SOFT COMPUTING Syllabus

CS2053                    SOFT COMPUTING                          L T P C
                                                                                                             3  0 0 3
 

UNIT I     FUZZY SET THEORY                                       10
Introduction  to Neuro – Fuzzy and Soft Computing – Fuzzy Sets – Basic Definition and
Terminology  –  Set-theoretic  Operations  –  Member  Function  Formulation  and
Parameterization – Fuzzy Rules and Fuzzy Reasoning – Extension Principle and Fuzzy
Relations  –  Fuzzy  If-Then  Rules  –  Fuzzy  Reasoning  –  Fuzzy  Inference  Systems  –
Mamdani  Fuzzy Models  – Sugeno  Fuzzy Models  –  Tsukamoto  Fuzzy Models  –  Input
Space Partitioning and Fuzzy Modeling.
             
UNIT II   OPTIMIZATION                       8
Derivative-based Optimization – Descent Methods – The Method of Steepest Descent –
Classical Newton’s Method – Step Size Determination – Derivative-free Optimization –
Genetic  Algorithms  –  Simulated  Annealing  –  Random  Search  –  Downhill  Simplex
Search.            

UNIT III  ARTIFICIAL INTELLIGENCE                 10
Introduction,  Knowledge  Representation  –  Reasoning,  Issues  and  Acquisition:
Prepositional and Predicate Calculus Rule Based  knowledge Representation Symbolic
Reasoning  Under  Uncertainity  Basic  knowledge  Representation  Issues  Knowledge
acquisition – Heuristic Search: Techniques for Heuristic search Heuristic Classification -
State  Space  Search:  Strategies  Implementation  of  Graph  Search  Search  based  on
Recursion Patent-directed Search Production System and Learning.    
 
UNIT IV   NEURO FUZZY MODELING                              9
Adaptive Neuro-Fuzzy  Inference Systems – Architecture – Hybrid Learning Algorithm –
Learning  Methods  that  Cross-fertilize  ANFIS  and  RBFN  –  Coactive  Neuro  Fuzzy
Modeling  –  Framework  Neuron  Functions  for  Adaptive  Networks  –  Neuro  Fuzzy
Spectrum.
           
UNIT V    APPLICATIONS OF COMPUTATIONAL INTELLIGENCE                        8
Printed  Character  Recognition  –  Inverse  Kinematics  Problems  –  Automobile  Fuel
Efficiency Prediction – Soft Computing for Color Recipe Prediction.  

                TOTAL: 45 PERIODS


TEXT BOOKS: 
1.  J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004,
Pearson Education 2004.
2.  N.P.Padhy,  “Artificial  Intelligence and  Intelligent Systems”, Oxford University Press,
2006.

REFERENCES:
1.   Elaine Rich & Kevin Knight,   Artificial  Intelligence, Second Edition, Tata Mcgraw Hill
Publishing Comp., 2006, New Delhi.
2.  Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.
3.  Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”,
Addison Wesley, N.Y., 1989.
4.  S.  Rajasekaran  and  G.A.V.Pai,  “Neural  Networks,  Fuzzy  Logic  and  Genetic
Algorithms”, PHI, 2003.
5.   R.Eberhart, P.Simpson and R.Dobbins, “Computational  Intelligence - PC Tools”, AP
Professional, Boston, 1996.
6.    Amit  Konar,  “Artificial  Intelligence  and  Soft  Computing  Behaviour  and  Cognitive
model of the human brain”, CRC Press, 2008.