Introduction To Soft Computing detail syllabus for Computer Science Engineering (CSE), 2019-20 scheme is taken from AKTU official website and presented for AKTU students. The course code (KOE046), 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 cse 4th sem syllabus for b.tech 2019-20 scheme aktu you can visit CSE 4th Sem syllabus for B.Tech 2019-20 Scheme AKTU Subjects. For all the other Select Subject-3 subjects do refer to Select Subject-3. The detail syllabus for introduction to soft computing is as follows.
Unit I
Introduction to Soft Computing, ARTIFICIAL NEURAL NETWORKS Basic concepts-Single layer perception-Multilayer Perception-Supervised and Unsupervised learning-Back propagation networks-Kohnen’s self-organizing networks-Hopfield network.
Unit II
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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
GENETIC ALGORITHMS Survival of the Fittest-Fitness Computations-Cross over-Mutation-Reproduction-Rank method-Rank space method.
Unit V
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Course Outcomes:
- 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.
Reference 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 Cse, 2019-20 regulation do visit Cse 4th Sem syllabus for 2019-20 Regulation.
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