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基于改進(jìn)粒子群與神經(jīng)網(wǎng)絡(luò)的機(jī)械結(jié)合面法向剛度建模
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國家重大科技專項(xiàng)資助項(xiàng)目（2009ZX04014-32）、國家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973計(jì)劃)資助項(xiàng)目（2009CB724406）和陜西省科學(xué)研究計(jì)劃資助項(xiàng)目（09JK669）


Modeling of Machined Joints Normal Stiffness Using Modified PSO-BP Neural Network Algorithm
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    摘要:

    為了提高機(jī)械結(jié)合面法向接觸剛度預(yù)測精度，提出一種改進(jìn)粒子群優(yōu)化算法，并用改進(jìn)粒子群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的參數(shù)組合，實(shí)現(xiàn)了粒子群和BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的算法模型。將影響結(jié)合面法向接觸剛度的因素進(jìn)行了特征分析和定量化描述，并用該算法進(jìn)行法向接觸剛度預(yù)測和相對誤差分析。計(jì)算結(jié)果表明，計(jì)算準(zhǔn)確度可達(dá)92%，實(shí)現(xiàn)了多種影響因素組合下的機(jī)械結(jié)合面法向接觸剛度的建模。

    Abstract:

    With the aim to improve forecasting accuracy of the normal contact stiffness of machined joints, the modified particle swarm optimizer (MPSO) algorithm was proposed. The BP neural network parameters were optimized by the MPSO algorithm. The normal contact stiffness of machined joints was forecasted under different experimental conditions, and the relative errors were analyzed. The results showed that the forecast precision could reach to 92%, and the contact stiffness of machined joints was modeled for various affecting factors. 

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楊紅平,傅衛(wèi)平,師彪,王雯,楊世強(qiáng),王偉.基于改進(jìn)粒子群與神經(jīng)網(wǎng)絡(luò)的機(jī)械結(jié)合面法向剛度建模[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(3):219-223,233. Yang Hongping,Fu Weiping, Shi Biao, Wang Wen, Yang Shiqiang, Wang Wei. Modeling of Machined Joints Normal Stiffness Using Modified PSO-BP Neural Network Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(3):219-223,233.

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