Pattern recognition using neural-fuzzy networks based on improved particle swam optimization

Authors:
Cheng-Jian Lin;Jun-Guo Wang;Chi-Yung Lee
Affiliations:
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung County, Taiwan 411, R.O.C;Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung County, Taiwan 413, R.O.C;Department of Computer Science and Information Engineering, Nankai Institute of Technology, Nantou, Taiwan 542, R.O.C
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper introduces a recurrent neural-fuzzy network (RNFN) based on improved particle swarm optimization (IPSO) for pattern recognition applications. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional PSO. A novel MEDO combining the evolutionary direction operator (EDO) and the migration operation is also proposed. Hence, the proposed IPSO method can improve the ability of searching global solution. Experimental results have shown that the proposed IPSO method has a better performance than the traditional PSO in the human body classification and the skin color detection.