论文信息 - Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis - 字舞流文

Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis

Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight ...

Xiaohua Wu | Da-Wei Zhang | Weilin Li | Xiaofeng Lv | Xiaohua Wu | Weilin Li | Xiaofeng Lv | Dawei Zhang

[1] Yang Yang,et al. Digital Generator Control Unit Design for a Variable Frequency Synchronous Generator in MEA , 2018 .

[2] Wenjie Liu,et al. A fast arc fault detection method for AC solid state power controllers in MEA , 2018 .

[3] Xiaobin Zhang,et al. A Novel Solid-State Circuit Breaker for On-Board DC Microgrid System , 2019, IEEE Transactions on Industrial Electronics.

[4] Shin'ichi Tamura,et al. Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

[5] Weilin Li,et al. State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer , 2017, IEEE Transactions on Industrial Electronics.

[6] Xin Yao,et al. A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[7] Silvio Simani,et al. Fault diagnosis in power plant using neural networks , 2000, Inf. Sci..

[8] Bruce A. Whitehead,et al. Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction , 1996, IEEE Trans. Neural Networks.