EE8012 - SOFT COMPUTING TECHNIQUES (Syllabus) 2017-regulation Anna University

EE8012 - SOFT COMPUTING TECHNIQUES (Syllabus) 2017-regulation Anna University

EE8012

SOFT COMPUTING TECHNIQUES

LPTC

3003

OBJECTIVES: To impart knowledge about the following topics:
• Basics of artificial neural network.
• Concepts of modelling and control of neural and fuzzy control schemes.
• Features of hybrid control schemes.

UNIT I

ARTIFICIAL NEURAL NETWORK

9

Review of fundamentals – Biological neuron, artificial neuron, activation function, single layer perceptron – Limitation – Multi layer perceptron – Back Propagation Algorithm (BPA) – Recurrent Neural Network (RNN) – Adaptive Resonance Theory (ART) based network – Radial basis function network – online learning algorithms, BP through time – RTRL algorithms – Reinforcement learning.

UNIT II

NEURAL NETWORKS FOR MODELING AND CONTROL

9

Modelling of non-linear systems using ANN – Generation of training data – Optimal architecture– Model validation – Control of non-linear systems using ANN – Direct and indirect neuro control schemes – Adaptive neuro controller – Familiarization with neural network toolbox.


UNIT III

FUZZY SET THEORY          

9

Fuzzy set theory – Fuzzy sets – Operation on fuzzy sets – Scalar cardinality, fuzzy cardinality, union and intersection, complement (Yager and Sugeno), equilibrium points, aggregation, projection, composition, cylindrical extension, fuzzy relation – Fuzzy membership functions.

UNIT IV

FUZZY LOGIC FOR MODELING AND CONTROL

9

Modelling of non-linear systems using fuzzy models – TSK model – Fuzzy logic controller – Fuzzification – Knowledge base – Decision making logic – Defuzzification – Adaptive fuzzy systems – Familiarization with fuzzy logic toolbox.

UNIT V

HYBRID CONTROL SCHEMES

9

Fuzzification and rule base using ANN – Neuro fuzzy systems – ANFIS – Fuzzy neuron– GA – Optimization of membership function and rule base using Genetic Algorithm – Introduction to other evolutionary optimization techniques, support vector machine– Case study – Familiarization with ANFIS toolbox.

TOTAL : 45 PERIODS

OUTCOMES:
 Ability to understand the concepts of ANN, different features of fuzzy logic and their modelling, control aspects and different hybrid control schemes.
 Ability to understand the basics of artificial neural network.
 Ability to get knowledge on modelling and control of neural.
 Ability to get knowledge on modelling and control of fuzzy control schemes.
 Ability to acquire knowledge on hybrid control schemes.
 Ability to understand the concepts of Adaptive Resonance Theory

TEXT BOOKS:
1. Laurence Fausett, “Fundamentals of Neural Networks”, Prentice Hall, Englewood Cliffs, N.J., 1992
2. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw Hill Inc., 2000.

REFERENCES
1. Goldberg, “Genetic Algorithm in Search, Optimization and Machine learning”, Addison Wesley Publishing Company Inc. 1989
2. Millon W.T., Sutton R.S. and Webrose P.J., “Neural Networks for Control”, MIT press, 1992
3. Ethem Alpaydin, “Introduction to Machine learning (Adaptive Computation and Machine Learning series)’, MIT Press, Second Edition, 2010.
4. Zhang Huaguang and Liu Derong, “Fuzzy Modeling and Fuzzy Control Series: Control Engineering”, 2006

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