【摘要】：分析了卡爾曼濾波在風(fēng)速序列預(yù)測分析中的應(yīng)用機(jī)理,構(gòu)造了用于風(fēng)速序列預(yù)測分析的遲滯神經(jīng)網(wǎng)絡(luò),并采用卡爾曼濾波方法將其與ARMA模型相融合,實(shí)現(xiàn)了風(fēng)速序列的混合預(yù)測。通過修改激勵(lì)函數(shù)的方式將遲滯特性引入神經(jīng)網(wǎng)絡(luò),網(wǎng)絡(luò)的權(quán)值采用梯度尋優(yōu)的方式確定,遲滯參數(shù)利用遺傳算法進(jìn)行確定。系統(tǒng)的狀態(tài)方程采用ARMA模型建立,將遲滯神經(jīng)網(wǎng)絡(luò)對(duì)風(fēng)速序列的預(yù)測結(jié)果作為測量方程的測量值。混合預(yù)測方法能減小單一預(yù)測機(jī)制造成的同一性質(zhì)誤差的累積。仿真實(shí)驗(yàn)結(jié)果表明,遲滯神經(jīng)網(wǎng)絡(luò)的預(yù)測性能優(yōu)于傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò),而混合預(yù)測方法的預(yù)測性能優(yōu)于單一預(yù)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(xué)電工電能新技術(shù)天津市重點(diǎn)實(shí)驗(yàn)室;天津工業(yè)大學(xué)電氣工程與自動(dòng)化學(xué)院;北京科技大學(xué)數(shù)理學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61203302)
【分類號(hào)】：TM614;TP183

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本文編號(hào)：2237941