Vibration prediction of a hydro-power house base on IFA-BPNN

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (24) : 64-69.

SONG Zhiqiang, GENG Dan, SU Chenhui, LIU Yunhe

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Abstract

A hydro-power house vibration prediction model was built based on the BP neural network optimized with the improved firefly algorithm (IFA-BPNN). Aiming at some disadvantages of FA including slow convergence, and easy to fall in local optimal values, a dynamic random local searching algorithm was introduced to speed up the convergent velocity, and do some mutation operations to avoid the optimization to fall into local optimal values. A dynamic step length updating measure was proposed to improve the accuracy of the optimization, and avoid the optimal solutions’ oscillation problem. Simulation examples showed that the prediction accuracy and convergent speed of the IFA-BPNN method are obviously improved, it can be used to predict vibration responses of a hydro-power house.

Key words

hydro-power house / vibration / improved firefly algorithm (IFA) / back-propagation neural network (BPNN)

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SONG Zhiqiang, GENG Dan, SU Chenhui, LIU Yunhe. Vibration prediction of a hydro-power house base on IFA-BPNN[J]. Journal of Vibration and Shock, 2017, 36(24): 64-69

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