ECE 539 Introduction to Artificial Neural Networks and Fuzzy Systems

3 graduate credits

Instructor: Professor Yu Hen Hu
Phone: 608/262-6724
E-mail: hu@engr.wisc.edu

Course objective:
This course covers basic concepts of artificial neural networks, fuzzy logic systems and their applications. Its focus will be on the introduction of basic theory, algorithm formulation and ways to apply these techniques to solve real world problems.

Course description:
Theory and applications of artificial neural networks and fuzzy logic: multilayer 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.

Prerequisite:
Knowledge of calculus, and basic probability and statistics are required. Background in the following subjects desirable: numerical analysis (including optimization), system theory, signals and systems. Programming skills in one of the following would be desirable: Matlab, MathCad, C, Java, C++, Pascal, Basic.

Homework:
Three-four assignments.

Exams:
Take-home final exam.

Computer facilities:
Internet access is required. Notes, homework, examination, software and projects are administered through course homepage. E-mail is the course's essential means for communication.

Textbook:
Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice Hall, New Jersey, 1999 (required).

Course notes:
None.

Project:
Described on course homepage.