农艺学
Extreme Learning Machine Based on Particle Swarm Optimization for Estimation of Reference Evapotranspiration
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.
领 域:
0 1
下载全文(PDF文件/1024K)
第36届中国控制会议论文集(C)
2017年
相似文献
On the Management Field
会议
Generalized Quantum Neural Predictive Networks
会议
Simulation Research on Two-Phase Flow Velocity Measurement System Based on Monte Carlo Method
会议
Changing Supply Function in Superposition Form and Its Applications
会议
Quantum Immune Clonal Algorithm for No-Idle Flow Shop Scheduling Problem
会议
Quantum Mechanics Helps in Searching for a Needle in a Haystack
会议
更多相似文献