Intelligent Control (Taught in English, 2021)

References :

National Taiwan University of Science and Technology
Department of Electrical Engineering
Fall, 2021
(Taught in English)


Prerequisite : None.
Instructor : Shun-Feng Su
Office : T2 502-3, Phone: 02-27333141 ext 6704,
E-mail :
Class Time : Friday 9:10~12:10
Classroom : TBD
Textbook : None.
Class note is available in Please select the course information (課程資訊) and the click the Intelligent control icon to download the rar file.
References : C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice-Hall, 1996.
J. S. R. Jang, C.-T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, 1997.
B. Kosko, Neural Networks and Fuzzy Systems, A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, 1992.
Tests : One Midterm and one final report.
Grades : Don’t worry! Be happy as long as you spend time on this course and turn in assignments as requird.


Course Description

The area of intelligent control is a fusion of a number of research areas in Systems and Control, Computer Science, Operation Research among others coming together, merging and expanding in new directions and opening new horizons to address the new problems of this challenging and promising area. Theoretical advances in this field have been made in many directions. This course is intended to make an understanding of the various mathematical formalisms used in intelligent control accessible to students. Particularly, three topics are discussed; they are fuzzy systems, neural networks, and evolutionary computation. Hopefully, this course can provide students the knowledge and the idea on this ongoing research area and encourage the initiation of the research on the applications of intelligent control to various fields.

Tentative Outline

  • Introduction of Intelligent Control 
  • Fuzzy Set Theorems and Fuzzy Reasoning
  • Fuzzy Logic Control and Fuzzy Modeling
  • Machine Learning and Neural Networks
  • Unsupervised Learning Schemes
  • Fuzzy Neural Integration 
  • Genetic Algorithms and Optimal Search