A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM

Authors:
Qi Wu
Affiliations:
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China and Key Laboratory of Measurement and Control of Complex Systems ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic system theory, this paper proposes new PSO method that uses chaotic mappings for parameter adaptation of Wavelet v-support vector machine (Wv-SVM). Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using logistic mapping sequences which increases its convergence rate and resulting precision. The simulation results show the parameter selection of Wv-SVM model can be solved with high search efficiency and solution accuracy under the proposed PSO method.