{"id":43,"title":"Theory of Fuzzy Systems","credit_hour":"3","lecture_hour":"45","outcome":"<!DOCTYPE html>\r\n<html>\r\n<head>\r\n<\/head>\r\n<body>\r\n<ul>\r\n<li>Learn about formal methods to represent “vague” and “less” mathematical knowledge.<\/li>\r\n<li>Formalize and systematic approach to represent and control a large class of nonlinear dynamical systems.<\/li>\r\n<li>Combine some of the traditional design approaches with fuzzy-logic concepts.<\/li>\r\n<li>Design fuzzy-logic based controllers and explore their unique characteristics.<\/li>\r\n<li>Exposure with the new and exciting applications of “vague” knowledge processing and experience the impact on popular dynamical systems.<\/li>\r\n<\/ul>\r\n<\/body>\r\n<\/html>","description":"<!DOCTYPE html>\r\n<html>\r\n<head>\r\n<\/head>\r\n<body>\r\n<p style=\"text-align: justify;\">This course serves as an introduction to soft computing, fuzzy systems, neural networks and neuro-fuzzy systems. Emphasis is placed on the fundamental concepts of fuzzy theory: set theory (fuzzy union, intersection, and complement), MF formulation and parameterization, fuzzy rules and fuzzy reasoning, regression and optimization. Additional topics covered include supervised learning neural networks, fuzzy inference systems, neuro-fuzzy systems modeling and control, ANFIS and its applications.<\/p>\r\n<\/body>\r\n<\/html>","books":"<!DOCTYPE html>\r\n<html>\r\n<head>\r\n<\/head>\r\n<body>\r\n<ol>\r\n<li><a href=\"https:\/\/www.amazon.com\/Neuro-Fuzzy-Soft-Computing-Computational-Intelligence\/dp\/0132610663\" target=\"_blank\"><em>Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence <\/em>by J.S.R. Jang, C.T. Sun, and E. Mizutani<\/a><\/li>\r\n<li><a href=\"https:\/\/www.amazon.com\/Fuzzy-Logic-Engineering-Applications-Third\/dp\/047074376X\" target=\"_blank\"><em>Fuzzy Logic with Engineering Applications<\/em> by Timothy J. Ross<\/a><\/li>\r\n<li><a href=\"https:\/\/www.amazon.com\/Introduction-Fuzzy-Logic-Practical-Applications\/dp\/0387948074\" target=\"_blank\"><em>An Introduction to Fuzzy Logic for Practical Applications<\/em> by T. Niimura<\/a><\/li>\r\n<\/ol>\r\n<\/body>\r\n<\/html>","created_at":"2016-09-03 13:07:57","updated_at":"2017-01-25 11:35:23"} Details | Department of Computer Science and Engineering, IUB

CEN 423: Theory of Fuzzy Systems

Offered Under: B.Sc. in Computer Engineering (CEN)

Description

This course serves as an introduction to soft computing, fuzzy systems, neural networks and neuro-fuzzy systems. Emphasis is placed on the fundamental concepts of fuzzy theory: set theory (fuzzy union, intersection, and complement), MF formulation and parameterization, fuzzy rules and fuzzy reasoning, regression and optimization. Additional topics covered include supervised learning neural networks, fuzzy inference systems, neuro-fuzzy systems modeling and control, ANFIS and its applications.

Prerequisites:

CEN 306 + Lab : Algorithms

Course Type	Major
Credit Hour	3
Lecture Hour	45

Expected Outcome(s):

Learn about formal methods to represent “vague” and “less” mathematical knowledge.
Formalize and systematic approach to represent and control a large class of nonlinear dynamical systems.
Combine some of the traditional design approaches with fuzzy-logic concepts.
Design fuzzy-logic based controllers and explore their unique characteristics.
Exposure with the new and exciting applications of “vague” knowledge processing and experience the impact on popular dynamical systems.

Suggested Books:

Grading Policy:

Biweekly Quiz, One Midterm Exam, One Final Exam, Project

Letter Grade	Marks	Grade Point
A	90 - 100	4.00
A-	85 - 89	3.70
B+	80 - 84	3.30
B	75 - 79	3.00
B-	70 - 74	2.70
C+	65 - 69	2.30
C	60 - 64	2.00
C-	55 - 59	1.70
D+	50 - 54	1.30
D	45 - 49	1.00
F	00 - 44	0.00