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Sctsyll

The document outlines a course on Mechatronics, focusing on various neural network models, including Radial Basis Function Networks and Adaptive Neuro-Fuzzy Inference Systems. It includes a detailed lecture schedule covering topics such as fuzzy logic, genetic algorithms, and applications in areas like character recognition and fuel efficiency prediction. Textbooks and reference materials are also listed to support the course content.

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whitedeath377
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46 views2 pages

Sctsyll

The document outlines a course on Mechatronics, focusing on various neural network models, including Radial Basis Function Networks and Adaptive Neuro-Fuzzy Inference Systems. It includes a detailed lecture schedule covering topics such as fuzzy logic, genetic algorithms, and applications in areas like character recognition and fuel efficiency prediction. Textbooks and reference materials are also listed to support the course content.

Uploaded by

whitedeath377
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Module 4 (9 Hours)

MECHATRONICS
Radial Basis Function Networks – Unsupervised Learning Neural Networks – Competitive Learning
Networks – Kohonen Self-Organizing Networks – Learning Vector Quantization –Hebbian learning.

Module 5 (9 Hours)

Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm – Learning


Methods that Cross- fertilize ANFIS and RBFN – Coactive Neuro Fuzzy Modeling–– Printed Character
Recognition – Inverse Kinematics Problems– Automobile Fuel Efficiency Prediction – Soft Computing
for Color Recipe Prediction

Text Books

1. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004, Pearson
Education 2004.
2. S.N.Sivanandam&S.N.Deepa “Principles of Soft Computing” Wiley India Pvt. Ltd., 2007

Reference Books

1. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.


2. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”,
Addison Wesley, N.Y., 1989.
3. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI,
2003.
4. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP
Professional, Boston, 1996.

Course Contents and Lecture Schedule

No Topic No. of Lectures

1 Introduction to Neuro

1.1 Fuzzy and Soft Computing 1 Hour

1.2 Fuzzy Sets ,Basic Definition and Terminology 1 Hour

1.3 Set-theoretic Operations 1 Hour

1.4 Member Function Formulation and Parameterization 2 Hours

1.5 Fuzzy Rules and Fuzzy Reasoning 2 Hours

1.6 Extension Principle and Fuzzy Relations 2 Hours

2 Fuzzy Inference Systems

2.1 Mamdani Fuzzy Models 2 Hours

2.2 Sugeno Fuzzy Models 2 Hours

2.3 Tsukamoto Fuzzy Models 2 Hours


2.4 Derivative-based Optimization 3 Hours
MECHATRONICS
3 Genetic Algorithms

3.1 Genetic Algorithms 1 Hour

3.2 Simulated Annealing 1 Hour

3.3 Random Search ,Downhill Simplex Search 2 Hours

3.4 Learning Neural Networks , Perceptrons 2 Hours

3.5 Adaline 1 Hour

3.6 Back propagation MutilayerPerceptrons 2 Hours

4 Radial Basis Function Network

4.1 Unsupervised Learning Neural Networks 2 Hours

4.2 Competitive Learning Networks 2 Hours

4.3 Kohonen Self-Organizing Networks 1 Hour

4.4 Learning Vector Quantization 2 Hours

4.5 Hebbian learning 2 Hours

5 Adaptive Neuro-Fuzzy Inference Systems

5.1 Architecture 1 Hour

5.2 Hybrid Learning Algorithm ,Learning Methods 1 Hour

5.3 fertilize ANFIS and RBFN 2 Hours

5.4 Coactive Neuro Fuzzy Modeling 1 Hour

5.5 Printed Character Recognition – Inverse Kinematics Problems 1 Hour

5.6 Automobile Fuel Efficiency Prediction 1 Hour

5.7 Soft Computing for Color Recipe Prediction 2 Hours

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