ECE 69000 - Neural Fuzzy Systems

Course Details

Credits: 3

Areas of Specialization:

  • Automatic Control

Counts as:

Normally Offered:

Spring - even years

Catalog Description:

This course provides students with insights into the basics of modeling, analysis, design and realization of neural-fuzzy systems. Topics include fuzzy set operations, fuzzy relations, fuzzy measures, fuzzy logic and reasoning; fuzzification, defuzzification, and inference engine in fuzzy logic control and decision systems; connectionist network models, supervised, unsupervised and reinforcement learning of neural networks and finally, structure and learning schemes of neural-fuzzy systems.

Required Text(s):

  1. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems , Chin-Teng Lin and C.S. George Lee , Prentice Hall PTR , 1996 , ISBN No. 0-13-25169-2

Recommended Text(s):

None.

Lecture Outline:

Weeks Topic
3 1. Review of Fuzzy Sets A. Basic Concepts of Fuzzy Sets B. t-norms, t-conorms, and Complement Functions C. Operations on Fuzzy Sets and Fuzzy Logic D. Fuzzy Relations and Fuzzy Relations Equations
1 2. Fuzzy Logic and Approximate Reasoning A. Multivalued Logic and Fuzzy Logic B. Approximate Reasoning
3 3. Analysis and Design of Fuzzy Logic Control Systems A. System Structure and Components of FLC Systems B. Fuzzification and Defuzzification Strategies C. Fuzzy Inference Engine D. Case Studies and Illustrating Examples
3 4. Connectionist Models A. Review of Neural Network Models B. Multilayer Networks (Supervised Learning) C. Unsupervised Learning Networks
4 5. Neural Fuzzy Systems A. Comparison of Fuzzy Systems and Neural Networks B. General Approaches Integrating Fuzzy Systems and Neural Networks C. Connectionist Model for Fuzzy Logic Control and Decision Systems D. Fuzzy-Logic-Based Neural Network Models E. Integrated Fuzzy Neural Models for Pattern Recognition F. Fuzzy Cerebellar Model Articulation Controllers
1 Two 1 Hour Exams

Assessment Method:

none