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CS157 Soft & Evolutionary Computing

CS157 Soft & Evolutionary Computing is a 3 credit course that introduces the basic concepts of soft computing and its application areas, particularly to intelligent systems. The course objectives are to understand what soft computing is and how it can be applied to intelligent systems like neuro-fuzzy systems and adaptive control systems. The course covers 5 units that teach the fundamentals of neural networks, backpropagation networks, fuzzy logic, genetic algorithms, and their applications.

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90 views1 page

CS157 Soft & Evolutionary Computing

CS157 Soft & Evolutionary Computing is a 3 credit course that introduces the basic concepts of soft computing and its application areas, particularly to intelligent systems. The course objectives are to understand what soft computing is and how it can be applied to intelligent systems like neuro-fuzzy systems and adaptive control systems. The course covers 5 units that teach the fundamentals of neural networks, backpropagation networks, fuzzy logic, genetic algorithms, and their applications.

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MESHAK
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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CS157 Soft & Evolutionary Computing

L–T–P-Cr: 3–0–0-3
Objectives: Introduce the basic of soft computing and its application areas particularly to intelligent
systems.
Pre-requisite: Artificial Intelligence, Intelligent systems
Outcome: By the end of this course, the students should know what Soft computing is and its application
areas particularly to Intelligent systems like neuro-fuzzy systems and adaptive control systems.
UNIT I Lectures: 8
Neural Networks-1(Introduction & Architecture)
Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions,
Neural network architecture: single layer and multilayer feed forward networks, recurrent
networks.Various learning techniques; perception and convergence rule, Auto-associative and hetro-
associative memory.
UNIT II Lectures: 8
Neural Networks-II (Back propogation networks)

Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception
model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm,
factors affecting backpropagation training, applications.
UNIT III Lectures: 8
Fuzzy Logic-I (Introduction)
Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set theory and operations, Properties of fuzzy
sets, Fuzzy and Crisp relations, Fuzzy to Crisp conversion.
UNIT IV Lectures: 8
Fuzzy Logic –II (Fuzzy Membership, Rules)
Membership functions, interference in fuzzy logic, fuzzy if-then rules, Fuzzy implications and Fuzzy
algorithms, Fuzzyfications and Defuzzificataions, Fuzzy Controller, Industrial applications of fuzzy logic.
UNIT V Lectures: 8
Genetic Algorithm (GA)
Basic concepts, working principle, procedures of GA, flow chart of GA, Genetic representations, (encoding)
Initialization and selection, Genetic operators, Mutation, Generational Cycle, applications.
Text Books:
1. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis
and Applications” Prentice Hall of India.
2. N.P.Padhy,”Artificial Intelligence and Intelligent Systems” Oxford University Press.
Reference Books:
3. Siman Haykin,”Neural Netowrks”Prentice Hall of India
4. Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India.
5. Kumar Satish, “Neural Networks” Tata Mc Graw Hill

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