Title :
Self-organizing fuzzy intelligent system
Author :
Li, Chunshien ; Lee, Chun-Yi
Author_Institution :
Dept. of Electr. Eng., Chang Gung Univ., Kwei-Shan, Taiwan
Abstract :
A self-organizing fuzzy system (SOFS) is presented. A plant model is not required for training, that is, the plant model is unknown to the SOFS. Using new data types, the vectors and matrices, a concise formulation is developed for the organization process and the inference activities of the SOFS. The fuzzy system can learn its rule-based structure and parameters from input/output training data. There is no fuzzy IF-THEN rule in the system initially. The fuzzy control policy is constructed automatically during the learning process when the system is simulated by input/output training data. With the well-known random optimization (RO) method, the generated fuzzy system can learn its parameters for specific applications. The proposed SOFS is applied to the temperature control problem.
Keywords :
fuzzy control; intelligent control; learning (artificial intelligence); self-adjusting systems; temperature control; clustering algorithm; fuzzy control systems; fuzzy system; inference activities; input/output training data; inverse learning control; learning process; random optimization; random optimization method; rule-based structure; self-learning; self-organization; self-organizing fuzzy intelligent system for temperature control; temperature control; training; water bath temperature control; Backpropagation algorithms; Clustering algorithms; Fuzzy control; Fuzzy systems; Humans; Intelligent systems; Optimization methods; Partitioning algorithms; Temperature control; Training data;
Conference_Titel :
Industry Applications Conference, 2002. 37th IAS Annual Meeting. Conference Record of the
Conference_Location :
Pittsburgh, PA, USA
Print_ISBN :
0-7803-7420-7
DOI :
10.1109/IAS.2002.1044128
