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Soft Computing Semester 7 (BE Fourth Year) BE Computer Engineering University of Mumbai Topics and Syllabus

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University of Mumbai Syllabus For Semester 7 (BE Fourth Year) Soft Computing: Knowing the Syllabus is very important for the students of Semester 7 (BE Fourth Year). Shaalaa has also provided a list of topics that every student needs to understand.

The University of Mumbai Semester 7 (BE Fourth Year) Soft Computing syllabus for the academic year 2021-2022 is based on the Board's guidelines. Students should read the Semester 7 (BE Fourth Year) Soft Computing Syllabus to learn about the subject's subjects and subtopics.

Students will discover the unit names, chapters under each unit, and subtopics under each chapter in the University of Mumbai Semester 7 (BE Fourth Year) Soft Computing Syllabus pdf 2021-2022. They will also receive a complete practical syllabus for Semester 7 (BE Fourth Year) Soft Computing in addition to this.

CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

University of Mumbai Semester 7 (BE Fourth Year) Soft Computing Revised Syllabus

University of Mumbai Semester 7 (BE Fourth Year) Soft Computing and their Unit wise marks distribution

University of Mumbai Semester 7 (BE Fourth Year) Soft Computing Course Structure 2021-2022 With Marking Scheme

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Syllabus

C Introduction to Soft Computing
  • Soft computing Constituents, Characteristics of Neuro Computing and Soft Computing, Difference between Hard Computing and Soft Computing, Concepts of Learning and Adaptation.
CC Neural Networks
201 Basics of Neural Networks
  • Introduction to Neural Networks, Biological Neural Networks, McCulloch Pitt model.
202 Supervised Learning Algorithms
  • Perceptron (Single Layer, Multi layer), Linear separability, Delta learning rule, Back Propagation algorithm.
203 Un-supervised Learning Algorithms
  • Hebbian Learning, Winner take all, Self Organizing Maps, Learning Vector Quantization.
CCC Fuzzy Set Theory
  • Classical Sets and Fuzzy Sets, Classical Relations and Fuzzy Relations, Properties of membership function, Fuzzy extension principle, Fuzzy Systems- fuzzification, defuzzification and fuzzy controllers.
CD Hybrid System
  • Introduction to Hybrid Systems, Adaptive Neuro Fuzzy Inference System (ANFIS).
D Introduction to Optimization Techniques
  • Derivative based optimization- Steepest Descent, Newton method.
  • Derivative free optimization- Introduction to Evolutionary Concepts.
DC Genetic Algorithms and Its Applications
  • Inheritance Operators, Cross over types, inversion and Deletion, Mutation Operator, Bit-wise Operators, Convergence of GA, Applications of GA.
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