本文選題：PSO　切入點(diǎn)：BSNN　出處：《電力系統(tǒng)保護(hù)與控制》2015年15期 　論文類型：期刊論文

【摘要】：考慮到風(fēng)的隨機(jī)性和波動(dòng)性,提出一種基于粒子群(PSO)優(yōu)化B樣條神經(jīng)網(wǎng)絡(luò)(BSNN)的短期風(fēng)速預(yù)測(cè)方法。利用相空間重構(gòu)方法確定BSNN的輸入空間向量,BSNN可以靈活地改變對(duì)輸入空間的劃分和對(duì)隱層基函數(shù)的定義,對(duì)任意的網(wǎng)絡(luò)輸入,隱層基函數(shù)的輸出只有少數(shù)非零,使網(wǎng)絡(luò)輸出簡單,收斂速度快。但在傳統(tǒng)的BSNN中,對(duì)輸入空間節(jié)點(diǎn)位置的均勻劃分是粗糙的,預(yù)測(cè)結(jié)果容易陷入局部極小而影響預(yù)測(cè)精度。粒子群優(yōu)化算法是一種智能搜索方法,它具有較強(qiáng)的搜索能力并且容易實(shí)現(xiàn),利用PSO優(yōu)化BSNN輸入空間的節(jié)點(diǎn)位置劃分,可避免BSNN陷入局部極小并提高預(yù)測(cè)精度。仿真結(jié)果表明,基于PSO-BSNN的預(yù)測(cè)模型比傳統(tǒng)的BSNN和BPNN預(yù)測(cè)模型具有更高的預(yù)測(cè)精度。
[Abstract]:Considering the randomness and volatility of the wind, A method of short-term wind speed prediction based on particle swarm optimization B-spline neural network (BSNN) is proposed. The method of phase space reconstruction is used to determine the input space vector of BSNN, which can flexibly change the partition of input space and the definition of hidden layer basis function. For arbitrary network input, the output of hidden layer basis function is only a few non-zero, which makes the network output simple and convergent speed. But in traditional BSNN, the uniform partition of node position in input space is rough. Particle Swarm Optimization (PSO) is an intelligent search method, which has strong searching ability and is easy to realize. PSO is used to optimize the node location partition of BSNN input space. The simulation results show that the prediction model based on PSO-BSNN has higher prediction accuracy than the traditional BSNN and BPNN models.
【作者單位】： 燕山大學(xué)電氣工程學(xué)院;
【基金】：河北省自然科學(xué)基金項(xiàng)目(F2012203088)~~
【分類號(hào)】：TP18;TM614
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本文編號(hào)：1598497