Principles of Soft Computing Course Outline - University of Sargodha

Course Title: Principles of Soft Computing

Course Code: CS-4863

Course Structure: Lectures: 3 / Labs: 0

Credit Hours: 3

Prerequisites: CS-3143 (Design and Analysis of Algorithms)

Course Objectives:

Soft Computing refers to a collection of computational techniques in computer science, artificial
intelligence and engineering disciplines which attempt to study, model and analyze complex
problems - those for which more conventional methods have not yielded low cost, analytic and
complete solutions. Unlike conventional computing, soft computing techniques are tolerant of
imprecision, uncertainty and approximations. This course introduces students with soft
computing techniques.

Course Syllabus:

Introduction to Soft Computing: Soft-Computing, Intelligent Systems and Soft Computing,
Importance, Decision Support Systems, Soft Computing for Smart Machine Design. Fuzzy Set

Theory: Fuzzy Systems, Fuzzy Sets, Fuzzy Logic, Fuzzy Rules/Relations, Membership
Functions, Fuzzification And Defuzzification, Fuzzy System Design, Fuzzy Arithmetics,
Decision Making With Fuzzy Information, Fuzzy Classification and Clustering. Neural
Networks: Single-Layer Networks, The Multi-Layer Perceptron, Radial Basis Functions, Error
Functions, Parameter Optimization Algorithms, Learning and Generalization, Bayesian Nets:
Symmetric Matrices, Dynamic Neural Networks and their Applications, Neuro-Fuzzy Systems.
Evolutionary Computation and Genetic Fuzzy Systems: Introduction GA For Problem Solving,
Theoretical Foundations. Machine Learning: Concept Learning and the General-to-Specific
Ordering, Decision Tree Learning, Evaluating Hypotheses, Computational Learning Theory,
Instance-Based Learning, Learning Sets Of Rules, Analytical Learning, Combining Inductive
And Analytical Learning.

Course Outline:

1.     Introduction to Soft Computing [TB1: Ch1]

2.      Soft-Computing: Introduction to Intelligent Systems and Soft Computing, Importance,
Decision Support Systems [TB1: Ch1], Soft Computing for Smart Machine Design [TB1:
Ch9].

3.      Fuzzy Set Theory - Fuzzy Systems: Fuzzy Sets [TB2:Ch 2], Fuzzy Logic, Fuzzy
Rules/Relations[TB2:Ch 3], Membership Functions, Fuzzification and Defuzzification
[TB2:Ch 4], Fuzzy System Design [TB2:Ch 8], Fuzzy Arithmetics [TB2:Ch 12], Decision
Making with Fuzzy Information[TB1:Ch 2], Fuzzy Classification and Clustering[TB2:Ch 10]

4.      Neural Networks: Single-Layer Networks[TB3: Ch. 3], The Multi-Layer Perceptron
[TB3:Ch4], Radial Basis Functions [TB3:Ch 5], Error Functions[TB3: Ch6], Parameter
Optimization Algorithms[TB3:Ch7], Learning and Generalization[TB3:Ch 9], Bayesian
Nets(Symmetric Matrices [TB3:Ch10], Dynamic Neural Networks and Their
Applications[TB1: Ch6], Neuro-Fuzzy Systems [TB1: Ch7]

5.      Evolutionary Computation And Genetic Fuzzy Systems: Introduction[TB4:Ch1, TB1:Ch8],
GA for Problem Solving[TB4:Ch2], Theoretical Foundations[TB4:Ch4]

6.      Machine Learning: Concept Learning and the General-To-Specific Ordering [TB5:Ch2],
Decision Tree Learning[TB5:Ch3], Evaluating Hypotheses[TB5:Ch5], Computational
Learning Theory[TB5:Ch7], Instance-Based Learning[TB5:Ch8], Learning Sets of
Rules[TB5:Ch10], Analytical Learning[TB5:Ch11], Combining Inductive and Analytical
Learning[TB 5:Ch12]

Textbook(s):

1.      Soft Computing and Intelligent Systems Design: Theory, Tools, and Applications by F.
Karray, C. De Silva, Addison-Wesley; 1^st Edition (June 4, 2004). ISBN-10: 0321116178

2.      Fuzzy Logic with Engineering Applications by T. Ross, Third Edition, Wiley; 3^rd Edition
(March 1, 2010). ISBN-10: 047074376X

3.      Neural Networks and Pattern Recognition by C. Bishop, Oxford University Press, (1996).
ISBN-10: 0198538642

4.      An Introduction to Genetic Algorithms by M. Mitchell. A Bradford Book; Third Printing
Edition (February 6, 1998). ISBN-10: 0262631857

5.      Machine Learning by T. Mitchell, McGraw-Hill Science/Engineering/Math; 1^st Edition
(March 1, 1997). ISBN-10: 0070428077

Reference Material:

An Introduction to Genetic Algorithms by M. Mitchell, A Bradford Book (February 6,
1998). ISBN-10: 0262631857

Genetic Algorithms in Search, Optimization, and Machine Learning by D. E. Goldberg ,
Addison-Wesley Professional; 1^st Edition (January 11, 1989). ISBN-10: 0201157675
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applicationsby S. V.
Kartalopoulos, IEEE Press - PHI, (2004).

Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications by S.
Rajasekaran & G. A. Vijayalakshmi Pai, PHI, (2003).

Principles of Soft Computing by S. N. Sivanandam & S. N. Deepa , Wiley - India, (2007).

Note: This content is obtained from official documents of University of Sargodha and applied on BS Computer Science for Main Campus, Sub Campuses, and Affiliated Colleges.