【摘要】：分析了卡爾曼濾波在風速序列預測分析中的應用機理,構造了用于風速序列預測分析的遲滯神經網絡,并采用卡爾曼濾波方法將其與ARMA模型相融合,實現了風速序列的混合預測。通過修改激勵函數的方式將遲滯特性引入神經網絡,網絡的權值采用梯度尋優(yōu)的方式確定,遲滯參數利用遺傳算法進行確定。系統(tǒng)的狀態(tài)方程采用ARMA模型建立,將遲滯神經網絡對風速序列的預測結果作為測量方程的測量值�；旌项A測方法能減小單一預測機制造成的同一性質誤差的累積。仿真實驗結果表明,遲滯神經網絡的預測性能優(yōu)于傳統(tǒng)BP神經網絡,而混合預測方法的預測性能優(yōu)于單一預測方法。
[Abstract]:The application mechanism of Kalman filter in wind speed series prediction and analysis is analyzed. A hysteretic neural network is constructed for wind speed series prediction and analysis, and the Kalman filter method is used to fuse it with ARMA model. The mixed prediction of wind speed series is realized. The hysteresis characteristic is introduced into the neural network by modifying the excitation function, the weights of the network are determined by gradient optimization, and the hysteresis parameters are determined by genetic algorithm. The ARMA model is used to establish the state equation of the system, and the prediction result of the hysteresis neural network to the wind speed series is taken as the measured value of the measurement equation. The mixed prediction method can reduce the accumulation of the same property error caused by a single prediction mechanism. Simulation results show that the prediction performance of hysteresis neural network is better than that of traditional BP neural network, and that of hybrid prediction method is better than that of single prediction method.
【作者單位】： 天津工業(yè)大學電工電能新技術天津市重點實驗室;天津工業(yè)大學電氣工程與自動化學院;北京科技大學數理學院;
【基金】：國家自然科學基金資助項目(61203302)
【分類號】：TM614;TP183

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