ECE 539: Introduction to Artificial Neural Network and Fuzzy Systems

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ECE 539 - Introduction to Artificial Neural Network and Fuzzy Systems

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ECE 539: Introduction to Artificial Neural Network and Fuzzy Systems, Fall 2005 (Hu), formerly Course Homepage of Hu

ECE 539: Introduction to Artificial Neural Network and Fuzzy Systems, Fall 2001 (Hu)

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Catalog Description

539 Introduction to Artificial Neural Network and Fuzzy Systems. (Crosslisted with Comp Sci, ME) I; Odd yrs; 3 cr (D). Theory and applications of artificial neural networks and fuzzy logic: multi-layer perceptron, self-organization map, radial basis network, Hopfield network, recurrent network, fuzzy set theory, fuzzy logic control, adaptive fuzzy neural network, genetic algorithm, and evolution computing. Applications to control, pattern recognition, nonlinear system modeling, speech and image processing. P: Comp Sci 302, or Comp Sci 310, or knowledge of C programming lang.

Course Prerequisite(s)

Comp Sci 302
Comp Sci 310

Prerequisite knowledge and/or skills

Math 340, or ECE 330, or consent of instructor. CS 302, or CS 310, or knowledge of C programming language.

Textbook(s) and/or other required material

Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice Hall, Upper Saddle River, NJ, SECOND EDITION, 1999 (required)

Fuzzy System Theory and Its Applications, T. Terano, K. Asai, and M. Sugeno, Academic Press, San Diego, CA, 1992. (optional)

A set of password-protected class notes will be available from the web site. Password will be given during class.

Course objectives

This course presents an overview of the theory and applications of artificial neural network and fuzzy systems to engineering applications with emphasis on signal processing and control. The objective of this course is on the understanding of various neural network and fuzzy systems models and the applications of these models to solve engineering problems.

Topics covered

Learning paradigms, perceptron learning
Multi-Layer Perceptron and Back-propagation learning
Pattern classification
Support vector machines
Clustering, Self-Organization Map
Radial Basis Network,
Time series analysis, system identification and expert system applications
Fuzzy Set Theory and Fuzzy Logic Control
Genetic Algorithm and Evolution Computing
Recurrent Network, Hopfield network

Class/laboratory schedule

(Sec. 1, on-campus, lecture) MWF 11:00-11:50 AM. Class room: 2534 Engr. Hall.
(Sec. 2, on-campus, video tape) MWF 5:00-6:00 PM, Class room 2321 Engr. Hall.
(Sec. 3, 4 off-campus, video tape)

Relationship of course to undergraduate degree program objectives and outcomes

This course serves students in a variety of engineering majors. The information below describes how the course contributes to the college's educational objectives.

knowledge in the basic techniques of mathematics and the physical sciences basic skill in methods of design and analysis across a broad range of electrical and computer engineering areas

Assessment of student progress toward course objectives

40% three to four assignments
20% Take home final exam
40% Indivisual Term Project

Person(s) who prepared this description

Yu Hen Hu

Copyright 2007 The Board of Regents of the University of Wisconsin System
Date last modified: 18-Jul-2007
Content by: ece@engr.wisc.edu
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