AN ADAPTIVE ONLINE SEQUENTIAL EXTREME LEARNING MACHINE FOR SHORT-TERM WIND SPEED PREDICTION BASED ON IMPROVED ARTIFICIAL BEE COLONY ALGORITHM

作     者：Tian, Z. Wang, G. Ren, Y. Li, S. Wang, Y. 

作者机构：Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China 

出 版 物：《NEURAL NETWORK WORLD》 (Neural Network World)

年 卷 期：2018年第28卷第3期

页      面：191-212页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Science Research Project of Liaoning Education Department [LGD2016009] Natural Science Foundation of Liaoning Province of China National Key R&D Program of China [2016YFD0700104-02] 

主　　题：extreme learning machine improved artificial bee colony algorithm adaptive online sequential short-term wind speed prediction 

摘      要：As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.