Soft Computing Techniques Med Ele 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective III) detail syllabus for Medical Electronics (Med Ele), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (BM8078), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other med ele 7th sem syllabus for be 2017 regulation anna univ you can visit Med Ele 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective III subjects do refer to Professional Elective III. The detail syllabus for soft computing techniques is as follows.
Course Objective:
The student should be made to
- Understand the different soft computing techniques.
- Understand neural network architectures and learning algorithms, for different applications
- Explore the use of Fuzzy and Genetic Algorithm
- Understand different Optimization techniques in soft computing
- To introduce Hybrid and Other advanced model in soft computing.
Unit I
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Unit II
Fuzzy Set Theory
Introduction to Fuzzy – 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 Modelling.
Unit III
Genetic Algorithm
Genetic Algorithms: Introduction to Genetic Algorithms (GA), Representation, Operators in GA, Fitness function, population, building block hypothesis and schema theorem.; Genetic algorithms operators methods of selection, crossover and mutation, simple GA (SGA), other types of GA, generation gap, steady state GA.
Unit IV
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Unit V
Hybrid and Advanced Model in Soft Computing
Genetic Algorithm based Back propagation Network, Fuzzy Logic Controlled Genetic Algorithms, Neuro-fuzzy hybrid systems, Support Vector Machine, Extreme Learning Machine (ELM), Extended ELM, Random Forest Algorithm.
Course Outcome:
Upon successful completion of the course student should be able to
- Describe various neural, fuzzy and Genetic algorithms.
- Implement Neural, Genetic and Fuzzy algorithms for various classification applications
Text Books:
- J.S.R.Jang, C.T.Sun and E.Mizutani, Neuro-Fuzzy and Soft Computing, PHI, 2004, Pearson Education 2004.
- James A Freeman and David M.Skapra, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley, 1991, Digital Version 2007.
- Davis E.Goldberg, Genetic Algorithms: Search, Optimization and MachineLearning, Addison Wesley, N.Y., 1989
References:
- LaureneFausett, Fundamentals of neural networks- Architectures, algorithms and applications, Prentice Hall, 1994.
- Simon O. Haykins,Neural Networks: A Comprehensive Foundation, 2nd Edition, Pearson 1994
- Zimmermann H.J. “Fuzzy set theory and its Applications” Springer international edition, 2011.
For detail syllabus of all other subjects of BE Med Ele, 2017 regulation do visit Med Ele 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.