The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis
| Autor: | Wei Jin Ma, Yan He, Ji Ping Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Engineering media_common.quotation_subject Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology Fault (power engineering) Machine learning computer.software_genre Inertia ComputingMethodologies_ARTIFICIALINTELLIGENCE 0203 mechanical engineering Computer Science::Computational Engineering Finance and Science 021105 building & construction Control parameters Selection (genetic algorithm) media_common Artificial neural network Optimization algorithm business.industry Particle swarm optimization 020303 mechanical engineering & transports ComputingMethodologies_PATTERNRECOGNITION lcsh:TA1-2040 Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) computer |
| Zdroj: | MATEC Web of Conferences, Vol 63, p 02019 (2016) |
| Popis: | The particle swarm optimization (PSO) is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO. |
| Databáze: | OpenAIRE |
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