Identification and Prediction Using Neuro-Fuzzy Networks with Symbiotic Adaptive Particle Swarm Optimization

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/6882

Title:	Identification and Prediction Using Neuro-Fuzzy Networks with Symbiotic Adaptive Particle Swarm Optimization
Authors:	林正堅
Contributors:	資訊工程系
Date:	2011-01
Issue Date:	2017-12-17 13:27:12 (UTC+8)
Abstract:	This study presents a novel symbiotic adaptive particle swarm optimization (SAPSO) for neuro-fuzzy network design. The proposed SAPSO uses symbiotic evolution and adaptive particle swarm optimization with neighborhood operator (APSO-NO) to improve the performance of the traditional PSO. In APSO-NO, we combine the neighborhood operator and the adaptive particle swarm optimization to tune the particles that are most significant. Simulation results have shown that the proposed SAPSO performs better and requires less computation time than the traditional PSO. (36 refs)
Relation:	Informatica
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

Files in This Item:

There are no files associated with this item.

View Licence