Multivariate nonstationary fluctuating wind velocity prediction using kernel-based extreme learning machine

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (18) : 223-230.

Zheng Xiaofen1 Zhong Wang2 Li Chunxiang2

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Abstract

By resorting to Fast Ensemble Empirical Mode Decomposition (FEEMD), the nonstationary downburst wind velocity sample can be decomposed into a series of intrinsic mode functions (IMFs). Subsequently, the hybridizing FEEMD and kernel-based extreme learning machine, referred to as the FEEMD-KELM, are proposed, so as to forecast training set and testing set partitioned to IMFs. For the purpose of comparison, the results of hybridizing FEEMD and extreme learning machine (FEEMD-ELM) are also taken into consideration. Comparing the results of these two predicting algorithms shows that the FEEMD-KELM renders more stable and higher accuracy than the FEEMD-ELM.

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Prediction / Extreme Learning Machines / Kernel-based Extreme Learning Machine Nonstationarity / Downburst / Fluctuating wind velocity / Fast Ensemble Empirical Mode Decomposition

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Zheng Xiaofen1 Zhong Wang2 Li Chunxiang2. Multivariate nonstationary fluctuating wind velocity prediction using kernel-based extreme learning machine[J]. Journal of Vibration and Shock, 2017, 36(18): 223-230

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