Introduction To Soft Computing detail syllabus for Chemical Engineering (CH), 2019-20 scheme is taken from AKTU official website and presented for AKTU students. The course code (KOE036), and for exam duration, Teaching Hr/Week, Practical Hr/Week, Total Marks, internal marks, theory marks, and credits do visit complete sem subjects post given below.
For all the other ch 3rd sem syllabus for b.tech 2019-20 scheme aktu you can visit CH 3rd Sem syllabus for B.Tech 2019-20 Scheme AKTU Subjects. For all the other Select Subject-1 subjects do refer to Select Subject-1. The detail syllabus for introduction to soft computing is as follows.
Course Outcomes:
At the end of course , the student will be able to understand
- Comprehend the fuzzy logic and the concept of fuzziness involved in various systems and fuzzy set theory.
- Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic
- Describe with genetic algorithms and other random search procedures useful while seeking global optimum in selflearning situations. K4
- Understand appropriate learning rules for each of the architectures and learn several neural network paradigms and its applications.
- Develop some familiarity with current research problems and research methods in Soft Computing Techniques.
Unit I
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Unit II
FUZZY SYSTEMS Fuzzy sets, Fuzzy Relations and Fuzzy reasoning, Fuzzy functions – Decomposition – Fuzzy automata and languages – Fuzzy control methods – Fuzzy decision making.
Unit III
NEURO – FUZZY MODELING Adaptive networks based Fuzzy interface systems – Classification and Regression Trees – Data clustering algorithms – Rule based structure identification – Neuro-Fuzzy controls – Simulated annealing – Evolutionary computation
Unit IV
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Unit V
APPLICATION OF SOFT COMPUTING Optimiation of traveling salesman problem using Genetic Algorithm, Genetic algorithm based Internet Search Techniques, Soft computing based hybrid fuzzy controller, Introduction to MATLAB Environment for Soft computing Techniques.
Text Books:
- An Introduction to Genetic Algorithm Melanic Mitchell (MIT Press)
- Evolutionary Algorithm for Solving Multi-objective, Optimization Problems (2nd Edition), Collelo, Lament, Veldhnizer ( Springer)
- Fuzzy Logic with Engineering Applications Timothy J. Ross (Wiley)
- Neural Networks and Learning Machines Simon Haykin (PHI)
- Sivanandam, Deepa, Principles of Soft Computing, Wiley
- Jang J.S.R, Sun C.T. and Mizutani E, “Neuro-Fuzzy and Soft computing”, Prentice Hall
- Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw Hill
- Laurene Fausett, “Fundamentals of Neural Networks”, Prentice Hall
- D.E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley
- Wang, Fuzzy Logic, Springer
For the detailed syllabus of all the other subjects of B.Tech Ch, 2019-20 regulation do visit Ch 3rd Sem syllabus for 2019-20 Regulation.
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