本文選題：量子遺傳算法　切入點：BP神經(jīng)網(wǎng)絡(luò)　出處：《環(huán)境工程學報》2016年11期

【摘要】：水質(zhì)預(yù)測對水環(huán)境規(guī)劃、評價和管理十分重要。構(gòu)建一種改進的量子遺傳算法(QGA)優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的模型,即在量子遺傳算法中引入了旋轉(zhuǎn)角的動態(tài)改進策略和遺傳算法的交叉變異操作,并以改進的QGA作為進化操作準則優(yōu)化BP模型的權(quán)值和閾值。以彌苴河復(fù)雜水環(huán)境水質(zhì)預(yù)測為實例,選取一組歷史觀測數(shù)據(jù)作為訓練樣本,對其進行分析。將結(jié)果與BP模型、QGA-BP模型仿真結(jié)果進行了對比,改進后的QGA-BP模型在進化代數(shù)、收斂速度和預(yù)測結(jié)果的準確率有較大提高。對彌苴河水質(zhì)的預(yù)測結(jié)果表明,將改進QGA-BP模型用于水質(zhì)預(yù)測是可行、有效的預(yù)測方法。
[Abstract]:Water quality prediction is very important for water environment planning, evaluation and management. An improved Quantum genetic algorithm (QGA) is proposed to optimize BP neural network. That is, the dynamic improvement strategy of rotation angle and the crossover mutation operation of genetic algorithm are introduced in quantum genetic algorithm. The weight and threshold of BP model are optimized by using the improved QGA as the evolutionary operating criterion, and a set of historical observation data is selected as the training sample, taking the water quality prediction of the complex water environment of the Miju River as an example. The results are compared with the simulation results of BP model and QGA-BP model. The improved QGA-BP model is improved greatly in evolutionary algebra, convergence rate and accuracy of prediction results. It is feasible and effective to apply the improved QGA-BP model to water quality prediction.
【作者單位】： 昆明理工大學信息工程與自動化學院;大理州洱海流域保護局;
【基金】：云南省科技廳科技惠民項目(2014RA051);云南省科技廳面上項目(2013FZ010)
【分類號】：X832;X52
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本文編號：1654062