Soft Computing Techniques Detailed Syllabus for B.Tech third year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for Soft Computing Techniques B.Tech 2016-2017 (R16) third year second sem is as follows.
B.Tech. III Year II Sem. L/T/P/C
Course Code:EM621OE 3/0/0/3
Prerequisite: Nil.
Course Objectives: This course makes the students to Understand
- Fundamentals of Neural Networks & Feed Forward Networks.
- Associative Memories & ART Neural Networks.
- Fuzzy Logic & Systems.
- Genetic Algorithms and Hybrid Systems.
Course Outcomes: On completion of this course the students will be able to
- Identify and employ suitable soft computing techniques in classification and optimization problems.
- Design hybrid systems to suit a given real – life problem.
UNIT –I: Fundamentals of Neural Networks & Feed Forward Networks: Basic Concept of Neural Networks, Human Brain, Models of an Artificial Neuron, Learning Methods, Neural Networks Architectures, Single Layer Feed Forward Neural Network :The Perceptron Model, Multilayer Feed Forward Neural Network :Architecture of a Back Propagation Network (BPN), The Solution, Back propagation Learning, Selection of various Parameters in BPN. Application of Back propagation Networks in Pattern Recognition & Image Processing.
UNIT –II: Associative Memories & ART Neural Networks: Basic concepts of Linear Associator, Basic concepts of Dynamical systems, Mathematical Foundation of Discrete-Time Hop field Networks(HPF), Mathematical Foundation of Gradient-Type Hopfield Networks, Transient response of Continuous Time Networks, Applications of HPF in Solution of Optimization Problem: Minimization of the Traveling salesman tour length, Summing networks with digital outputs, Solving Simultaneous Linear Equations, Bidirectional Associative Memory Networks; Cluster Structure, Vector Quantization, Classical ART Networks, Simplified ART Architecture.
UNIT –III: Fuzzy Logic & Systems: Fuzzy sets, Crisp Relations, Fuzzy Relations, Crisp Logic, Predicate Logic, Fuzzy Logic, Fuzzy Rule based system, Defuzzification Methods, Applications: Greg Viot’s Fuzzy Cruise Controller, Air Conditioner Controller.
TEXT BOOKS:
- Introduction to Artificial Neural Systems – J.M.Zurada, Jaico Publishers
- Neural Networks, Fuzzy Logic & Genetic Algorithms: Synthesis & Applications – S.Rajasekaran, G.A. Vijayalakshmi Pai, July 2011, PHI, New Delhi.
- Genetic Algorithms by David E. Gold Berg, Pearson Education India, 2006.
- Neural Networks & Fuzzy Sytems- Kosko.B., PHI, Delhi,1994.
REFERENCE BOOKS:
- Artificial Neural Networks – Dr. B. Yagananarayana, 1999, PHI, New Delhi.
- An introduction to Genetic Algorithms – Mitchell Melanie, MIT Press, 1998
- Fuzzy Sets, Uncertainty and Information- Klir G.J. & Folger. T. A., PHI, Delhi, 1993
For all other B.Tech 3rd Year 2nd Sem syllabus go to JNTUH B.Tech Electronics and Computer Engineering 3rd Year 2nd Sem Course Structure for (R16) Batch.
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