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Application of optimized neural network based on particle swarm optimization algorithm in fault diagnosis
In this paper an algorithm based on particle swarm optimization algorithm for RBF neural network is proposed. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic regulation of the number of radial basis function in neural network hidden layer, neural network structure is optimized. The algorithm is applied to gearbox fault diagnosis. Experimental results show the effectiveness and great performance. Classification effect of neural network based on particle swarm optimization algorithm is better than that of the RBF neural network for identifying effectively the different status of gearbox and monitoring timely the status changes of gearbox. Also it can reduce the time for fault diagnosis and improve accuracy of fault diagnosis.
Application of optimized neural network based on particle swarm optimization algorithm in fault diagnosis
In this paper an algorithm based on particle swarm optimization algorithm for RBF neural network is proposed. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic regulation of the number of radial basis function in neural network hidden layer, neural network structure is optimized. The algorithm is applied to gearbox fault diagnosis. Experimental results show the effectiveness and great performance. Classification effect of neural network based on particle swarm optimization algorithm is better than that of the RBF neural network for identifying effectively the different status of gearbox and monitoring timely the status changes of gearbox. Also it can reduce the time for fault diagnosis and improve accuracy of fault diagnosis.
Application of optimized neural network based on particle swarm optimization algorithm in fault diagnosis
Zhong, Bingxiang (author) / Wang, Debiao (author) / Li, Taifu (author)
2009
5 Seiten, 12 Quellen
Conference paper
English
| British Library Conference Proceedings | 2013
| American Institute of Physics | 2022