Tham khảo tài liệu 'an introduction to intelligent and autonomous control-chapter 9: fuzzy and neural control', công nghệ thông tin, quản trị web phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 9 Fuzzy and Neural Control Hamid R. Berenji Sterling Software Artificial Intelligence Research Branch NASA Ames Research Center Mountain View CA 94035 Abstract Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning. 1. INTRODUCTION AND MOTIVATION What is the fundamental difference between Fuzzy Logic Controllers FLCs and those that are based on conventional control theory How can FLCs learn and adaptively change their performance These questions are among the main questions that I will discuss in some details in this chapter. However to briefly answer the first question FLCs do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. As for the second question above in this chapter we consider neural networks to provide learning capability for FLCs although other learning methods of artificial intelligence may also be used. This chapter is not intended to provide a complete survey on either FLCs or applications of neural networks in control since other appropriate surveys on these topics are already available . see Berenji 8 Sugeno 29 Barto 5 and Antsaklis 2 . However in this chapter we will first cover some basics of fuzzy set theory and their application in designing FLCs. Next we discuss some issues related to the stabil 216 INTELLIGENT AND AUTONOMOUS CONTROL ity analysis of FLCs and some applications of this theory. Then we briefly describe the application of neural nets in control with a view toward a special family of techniques .
