A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM |
| |
| Authors: | Qi Wu |
| |
| Affiliation: | 1. University of Bergen, Institute of Physics, Norway;2. California Institute of Technology, United States;3. Università di Napoli Federico II, Italy;4. INFN Sezione di Napoli, Italy;5. Università degli Studi di Perugia, Italy;6. INFN Sezione di Perugia, Italy;7. Università di Roma La Sapienza, Italy;8. INFN Sezione di Roma, Italy;9. INFN Sezione di Roma Tre, Italy;1. Key Laboratory of Energy Thermal Conversion and Control of the Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China;2. Information Networking Institute, Carnegie Mellon University, Pittsburgh, PA 15217, USA;1. ReDCAD Laboratory, ENIS, University of Sfax, B.P. 1173, 3038 Sfax, Tunisia;2. Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, 3021 Sfax, Tunisia;1. The State Key Laboratory of Astronautic Dynamics (ADL), China Xi’an Satellite Control Center, Xi’an 710043, China;2. China Satellite Maritime Tracking and Control Department, Jiang Yin 214431, China;3. China Xi’an Satellite Control Center, Xi’an 710043, China |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
