Recurrent Fuzzy Neural System Design and Its Applications Using A Hybrid Algorithm of Electromagnetism-like and Back-propagation

• Main Authors: Fu-Kai Chang, 張富凱
• Other Authors: 李慶鴻
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/58699938852425541579

碩士 === 元智大學 === 電機工程學系 === 96 === Based on the electromagnetism-like algorithm (EM), we propose two kinds of novel hybrid learning algorithms. One is the improved EM algorithm with BP technique (IEMBP) and the other is the improved EM algorithm with GA technique (IEMGA) for recurrent fuzzy neural system design. IEMBP and IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and BP, EM and GA, respectively. EM algorithm is a population-based meta-heuristic originated from the electromagnetism theory. For recurrent fuzzy neural system design, IEMBP and IEMGA simulates the “accraction” and “repulsion” of charged particles by considering each neural system parameters as an electrical charge. The modification from EM algorithm is randomly the neighborhood local search substituted for BP, GA, and the competitive concept is adopted for training the recurrent fuzzy neural network (RFNN) system. IEMBP combines EM with BP to obtain high speed convergence and less computation complexity. However, it needs the system gradient information for optimization. For gradient information free system, IEMGA is proposed to treat the optimization problem. IEMGA consists of EM and GA to reduce the computation complexity of EM. IEMBP and IEMGA are used to develop the update laws of RFNN for nonlinear systems identification and control. Finally, several illustration examples are presented to show the performance and effectiveness of IEMBP and IEMGA.