北京科技大学学报Issue(8)：1108-1114,7.DOI:10.13374/j.issn1001-053x.2014.08.018
基于卡尔曼滤波的迟滞神经网络风速序列预测
Wind speed forecasting by a hysteretic neural network based on Kalman filtering
摘要
Abstract
The hysteretic characteristic was introduced into the activation functions of neurons, and a forward hysteretic neural net-work was proposed. In combination with the Kalman filter algorithm, the hysteretic neural network was applied to wind speed forecas-ting. A change rate series of wind speed was constructed according to the original wind speed time series. Forecasting analysis of both the series was performed with the hysteretic neural network, these prediction results were fused using the Kalman filter algorithm, and thus the optimal estimated results were obtained. Simulation results show that the hysteretic neural network has more flexible structure, better generalization ability, and better prediction performance than the conventional neural network. The prediction performance can be further improved by Kalman filter fusion.关键词
风力发电/风速/预测/神经网络/迟滞/卡尔曼滤波Key words
wind energy power generation/wind speed/forecasting/neural networks/hysteresis/Kalman filtering分类
信息技术与安全科学引用本文复制引用
李艳晴,修春波,张欣..基于卡尔曼滤波的迟滞神经网络风速序列预测[J].北京科技大学学报,2014,(8):1108-1114,7.基金项目
国家自然科学基金资助项目(61203302) （61203302）
