標題: USING MULTI-ANGLES EVOLUTIONARY ALGORITHMS FOR TRAINING TSK-TYPE NEURO-FUZZY NETWORKS
作者: Hung, Pei-Chia
Lin, Sheng-Fuu
Hsu, Yung-Chi
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Neuro-fuzzy network;Evolutionary algorithm;Multiple angles
公開日期: 1-Nov-2012
摘要: The development of a global-based method for building robust neuro-fuzzy networks has become an interesting issue. Among the various building methods, the evolutionary algorithms provide robust ways increasing the chances of meeting the optimal solution. However, evolutionary algorithms may only use a single angle to evaluate the searching space to obtain the optimal solutions. It implies that they may slowly or even hardly meet the optimal solution. Thus, the current study provides a novel architecture that uses multiple angles for evaluating the searching space. More specifically, the novel architecture adopts multiple angles to improve the evolutionary process by dynamically adjusting the searching space. By doing so, the proposed architecture can increase the chances of meeting the optimal solution. As shown in the results, the proposed architecture outperforms other existing evolutionary algorithms. Based on the results, a framework is proposed to build a benchmark for developing evolutionary algorithms that consider the multiple angles of the solution space.
URI: http://hdl.handle.net/11536/20633
ISSN: 1349-4198
期刊: INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
Volume: 8
Issue: 11
起始頁: 7793
結束頁: 7818
Appears in Collections:Articles