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《第36届中国控制会议论文集(C)》2017年
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Extreme Learning Machine Based on Particle Swarm Optimization for Estimation of Reference Evapotranspiration

Tianfeng Liu  Yongsheng Ding  Xin Cai  Yifeng Zhu  Xiangfei Zhang  
【摘要】:Reference evapotranspiration(ET_0) plays an important role in water resources scheduling of irrigation systems.This paper proposes a novel extreme learning machine(ELM) method optimized by particle swarm optimization(PSO) algorithm(PSO-SWELM) to realize more accurate evapotranspiration estimation with limited environmental and meteorological data.The weights and thresholds between input and hidden layers of ELM is optimized by PSO algorithm and a function based on the two-wave superposition is selected as the activation function of ELM,which both enhances the accuracy of estimation.The Penman-Monteith model(FAO-56 PM) is used as the standard model to estimate ET_0.The root of mean squared error(RMSE)and coefficient of determination(R2) are set as the two evaluation criteria to compare the performances of BP,PSO-BP,SVM,ELM,PSO-ELM and PSO-SWELM in estimating ET_0.The simulation results show that the PSO-SWELM method has better performance in predicting the ET_0 than the currently prevailing methods.

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