Short-term prediction of BP neural network optimized by improved particle swarm optimization algorithm

Quan Sun; Yuan Sun

doi:10.1117/12.2637168

15 June 2022 Short-term prediction of BP neural network optimized by improved particle swarm optimization algorithm

Quan Sun, Yuan Sun

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 1228511 (2022) https://doi.org/10.1117/12.2637168
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Salps algorithm was used to optimize the initial weights and thresholds of BP neural network, to speed up the BP neural network parameters of PID controller, and finally obtain the optimal parameters. The variable weight is integrated into the iterative process to expand the early search range and improve the late search accuracy. In Matlab2019 simulation environment, BP neural network and BP neural network optimized by Salps were compared in the prediction effect. The results show that the optimized BP neural network has higher prediction accuracy than the traditional BP neural network.

Citation Download Citation

Quan Sun and Yuan Sun "Short-term prediction of BP neural network optimized by improved particle swarm optimization algorithm", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 1228511 (15 June 2022); https://doi.org/10.1117/12.2637168

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