Combining SOM and evolutionary computation algorithms for RBF neural network training
| Autor: | R. J. Kuo, Zhen-Yao Chen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering Artificial neural network business.industry Particle swarm optimization 02 engineering and technology Machine learning computer.software_genre Hybrid algorithm Industrial and Manufacturing Engineering Evolutionary computation 020901 industrial engineering & automation Function approximation Artificial Intelligence Genetic algorithm 0202 electrical engineering electronic engineering information engineering Test functions for optimization 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis computer Algorithm Software |
| Zdroj: | Journal of Intelligent Manufacturing. 30:1137-1154 |
| ISSN: | 1572-8145 0956-5515 |
| DOI: | 10.1007/s10845-017-1313-7 |
| Popis: | This paper intends to enhance the learning performance of radial basis function neural network (RBFnn) using self-organizing map (SOM) neural network (SOMnn). In addition, the particle swarm optimization (PSO) and genetic algorithm (GA) based (PG) algorithm is employed to train RBFnn for function approximation. The proposed mix of SOMnn with PG (MSPG) algorithm combines the automatically clustering ability of SOMnn and the PG algorithm. The simulation results revealed that SOMnn, PSO and GA approaches can be combined ingeniously and redeveloped into a hybrid algorithm which aims for obtaining a more accurate learning performance among relevant algorithms. On the other hand, method evaluation results for four continuous test function experiments and the demand estimation case showed that the MSPG algorithm outperforms other algorithms and the Box–Jenkins models in accuracy. Additionally, the proposed MSPG algorithm is allowed to be embedded into business’ enterprise resource planning system in different industries to provide suppliers, resellers or retailers in the supply chain more accurate demand information for evaluation and so to lower the inventory cost. Next, it can be further applied to the intelligent manufacturing system to cope with real situation in the industry to meet the need of customization. |
| Databáze: | OpenAIRE |
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