Description

This course introduces soft computing methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth. The principal constituents of soft computing are fuzzy logic, neural network theory, and genetic algorithms.

Program

Computer Science

Objectives

1. Understand the difference between hard and soft computing methods.
2. Be able to apply several soft computing techniques for learning from experimental data.
3. Understand what fuzzy logic is and apply it in various applications.
4. Apply neural networks to solve soft computing problems
5. Use genetic algorithms to solve soft computing problems.

Textbook

S. N. Sivanandam and S. N. Deepa, Principles of Soft Computing, WILEY

Course Content

content serial	Description
1	Introduction to Optimization
2	Genetic Algorithms
3	Genetic Programming and Evolutionary Strategies
4	Introduction to Artificial Neural Networks
5	Applications of ANN
6	Neural Network Learning
7	7th week exam
8	Introduction to Fuzzy logic
9	Fuzzy Rules
10	Fuzzy Inference
11	Particle Swarm Optimization
12	12th week exam
13	Soft computing applications
14	Comparison of soft computing approaches
15	Revision
16	Final exam

Markets and Career

Generation, transmission, distribution and utilization of electrical power for public and private sectors to secure both continuous and emergency demands.
Electrical power feeding for civil and military marine and aviation utilities.
Electrical works in construction engineering.

Degree Bachelor
Code: CS464
Credit hrs: 3
Prequisites: CS366

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Soft Computing