本文選題：量子遺傳算法　切入點(diǎn)：BP神經(jīng)網(wǎng)絡(luò)　出處：《煤炭工程》2016年07期

【摘要】：針對(duì)刮板輸送機(jī)減速器故障類型多、診斷準(zhǔn)確率低的問題,基于量子遺傳算法理論,提出了一種基于量子遺傳算法的神經(jīng)網(wǎng)絡(luò)故障診斷方法。利用量子遺傳算法對(duì)神經(jīng)網(wǎng)絡(luò)權(quán)值、閾值進(jìn)行優(yōu)化,加快目標(biāo)的優(yōu)化求解。初步研究表明將量子遺傳算法與BP神經(jīng)網(wǎng)絡(luò)結(jié)合可以有效地解決神經(jīng)網(wǎng)絡(luò)收斂速度慢,易陷入局部最小等問題,有助于提高刮板輸送機(jī)減速器的故障診斷精度。
[Abstract]:Aiming at the problem that the reducer of scraper conveyer has many kinds of faults and low diagnostic accuracy, it is based on quantum genetic algorithm (QGA) theory. A neural network fault diagnosis method based on quantum genetic algorithm (QGA) is proposed. The weights and thresholds of neural network are optimized by quantum genetic algorithm (QGA). The preliminary research shows that the combination of quantum genetic algorithm and BP neural network can effectively solve the problems such as slow convergence rate and easy to fall into local minimum. It is helpful to improve the fault diagnosis accuracy of the reducer of scraper conveyor.
【作者單位】： 西安科技大學(xué)電氣與控制工程學(xué)院;神華寧煤業(yè)集團(tuán)礦山機(jī)械制造維修分公司;
【基金】：國家自然科學(xué)基金項(xiàng)目(51277149)
【分類號(hào)】：TD528.3

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