標題: A GA-based fuzzy adaptive learning control network
作者: Chung, IF
Lin, CJ
Lin, CT
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: fuzzy ART;expert systems;membership function;space partition;backpropagation;chaotic sequence
公開日期: 16-五月-2000
摘要: This paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. A structure/parameter learning algorithm, called FALCON-GA, is proposed for constructing the FALCON automatically. The FALCON-GA is a three-phase hybrid learning algorithm. In the first phase, the fuzzy ART algorithm is used to do fuzzy clustering in the input/output spaces according to the supervised training data. In the second phase, the genetic algorithm (GA) is used to find proper fuzzy logic rules by associating input clusters and output clusters. Finally, in the third phase, the backpropagation algorithm is used for tuning input:output membership functions. Hence, the FALCON GA combines the backpropagation algorithm for parameter learning and both the fuzzy ART and GAs for structure learning. It can partition the input/output spaces, tune membership functions and find proper fuzzy logic rules automatically. The proposed FALCON has two important features. First, it reduces the combinatorial demands placed by the standard methods for adaptive linearization of a system. Second, the FALCON is a highly autonomous system. In its learning scheme, only the training data need to be provided from the outside world. The users need not give the initial fuzzy partitions, membership functions and fuzzy logic rules. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system. (C) 2000 Elsevier Science B.V. All rights reserved.
URI: http://hdl.handle.net/11536/30516
ISSN: 0165-0114
期刊: FUZZY SETS AND SYSTEMS
Volume: 112
Issue: 1
起始頁: 65
結束頁: 84
顯示於類別:期刊論文


文件中的檔案:

  1. 000086088800005.pdf