基于改進型PSO-BP神經網絡算法的水環(huán)境質量評價
發(fā)布時間：2018-11-09 11:15
【摘要】：針對水環(huán)境污染的治理與保護,需要科學的水環(huán)境評價方法對水環(huán)境進行分類。我國目前采用單因子評價法,評價原則為“一票否決制”。這種評價方法具有簡單直觀,易于操作的優(yōu)勢。但是,單因子評價法在水質監(jiān)測數(shù)據(jù)上存在數(shù)據(jù)利用不從分,評價結果過于悲觀的缺點。本文基于云南省洱海流域信息化項目,針對洱源縣永安江的水質監(jiān)測數(shù)據(jù)采用主成分分析法、BP神經網絡、PSO-BP算法進行水質評價研究。研究過程中發(fā)現(xiàn)主成分分析法所構成的評價函數(shù)物理意義不明確,并且評價過程中不能對污染物中影響程度較大的指標進行重點評價。針對主成分分析法以上不足,改用人工神經網絡評價法對水質評價工作進行建模,采用BP神經網絡算法對水環(huán)境質量進行綜合評價。BP神經網絡算法具有良好的非線性映射和自學習能力,對具有非線性復雜關系的水環(huán)境質量評價工作,結果更有針對性、物理意義更明確。但是,BP神經網絡算法有易陷入局部極值點、收斂速度慢、泛化能力弱、對網絡初始化參數(shù)敏感的缺陷。針對BP神經網絡水質評價模型的缺陷,本文考慮使用粒子群算法對BP神經網絡的網絡參數(shù)進行優(yōu)化。由于粒子群算法具有全局搜索能力強的優(yōu)點,通過粒子群算法對神經網絡的連接參數(shù)進行優(yōu)化,彌補神經網絡算法對網絡參數(shù)初始化設置敏感以及容易陷入局部極小值點的不足。同時,粒子群算法易于實現(xiàn)、結構簡單,與其他算法容易結合;粒子群算法采用并行運算,運算速度快,資源利用率高。將兩種算法結合以后,提高了神經網絡算法的收斂精度和泛化能力。但是,在對BP神經網絡進行優(yōu)化的過程中引入了新的變量和迭代過程,也增加了算法的運行時間。最后,對粒子群算法中慣性權重衰減函數(shù)的改進,在保證評價算法收斂精度的條件下,提高了算法的收斂速度,減少算法運行時間。通過實驗仿真,驗證了改進的評價算法在收斂精度保持一定的條件下,減少了算法運行時間。
[Abstract]:For the treatment and protection of water environment pollution, it is necessary to classify the water environment by scientific water environment evaluation method. At present, the single factor evaluation method is adopted in our country, and the principle of evaluation is "one vote veto system". This evaluation method has the advantages of simple and intuitive, easy to operate. However, the single factor evaluation method has the disadvantage of not using the water quality monitoring data, and the evaluation result is too pessimistic. Based on the information project of Erhai River Basin in Yunnan Province, the water quality monitoring data of Yongan River in Eryuan County were evaluated by principal component analysis, BP neural network and PSO-BP algorithm. It was found that the physical meaning of the evaluation function constituted by principal component analysis was not clear, and the evaluation process could not focus on the indexes with greater influence on pollutants. In view of the deficiency of principal component analysis method, artificial neural network evaluation method is used to model water quality evaluation. The BP neural network algorithm is used to evaluate the water environment quality synthetically. The BP neural network algorithm has good nonlinear mapping and self-learning ability, and the result is more pertinence to the water environment quality evaluation work with nonlinear and complex relationship. The physical meaning is clearer. However, the algorithm of BP neural network is easy to fall into local extremum, slow convergence speed, weak generalization ability and sensitive to network initialization parameters. Aiming at the defects of the water quality evaluation model of BP neural network, particle swarm optimization (PSO) algorithm is considered to optimize the network parameters of BP neural network. Because particle swarm optimization (PSO) has the advantage of global searching ability, the connection parameters of neural network are optimized by PSO. The neural network algorithm is sensitive to the initialization of network parameters and is prone to fall into local minima. At the same time, particle swarm optimization algorithm is easy to realize, simple structure, easy to combine with other algorithms, particle swarm optimization algorithm uses parallel operation, fast operation speed, high resource utilization. After combining the two algorithms, the convergence accuracy and generalization ability of the neural network algorithm are improved. However, in the process of optimization of BP neural networks, new variables and iterative processes are introduced, and the running time of the algorithm is also increased. Finally, the improvement of inertia weight attenuation function in PSO can improve the convergence speed of the algorithm and reduce the running time of the algorithm under the condition of ensuring the convergence accuracy of the evaluation algorithm. The experimental results show that the improved evaluation algorithm can reduce the running time of the algorithm under the condition of keeping the convergence accuracy.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：X824;TP183
[Abstract]:For the treatment and protection of water environment pollution, it is necessary to classify the water environment by scientific water environment evaluation method. At present, the single factor evaluation method is adopted in our country, and the principle of evaluation is "one vote veto system". This evaluation method has the advantages of simple and intuitive, easy to operate. However, the single factor evaluation method has the disadvantage of not using the water quality monitoring data, and the evaluation result is too pessimistic. Based on the information project of Erhai River Basin in Yunnan Province, the water quality monitoring data of Yongan River in Eryuan County were evaluated by principal component analysis, BP neural network and PSO-BP algorithm. It was found that the physical meaning of the evaluation function constituted by principal component analysis was not clear, and the evaluation process could not focus on the indexes with greater influence on pollutants. In view of the deficiency of principal component analysis method, artificial neural network evaluation method is used to model water quality evaluation. The BP neural network algorithm is used to evaluate the water environment quality synthetically. The BP neural network algorithm has good nonlinear mapping and self-learning ability, and the result is more pertinence to the water environment quality evaluation work with nonlinear and complex relationship. The physical meaning is clearer. However, the algorithm of BP neural network is easy to fall into local extremum, slow convergence speed, weak generalization ability and sensitive to network initialization parameters. Aiming at the defects of the water quality evaluation model of BP neural network, particle swarm optimization (PSO) algorithm is considered to optimize the network parameters of BP neural network. Because particle swarm optimization (PSO) has the advantage of global searching ability, the connection parameters of neural network are optimized by PSO. The neural network algorithm is sensitive to the initialization of network parameters and is prone to fall into local minima. At the same time, particle swarm optimization algorithm is easy to realize, simple structure, easy to combine with other algorithms, particle swarm optimization algorithm uses parallel operation, fast operation speed, high resource utilization. After combining the two algorithms, the convergence accuracy and generalization ability of the neural network algorithm are improved. However, in the process of optimization of BP neural networks, new variables and iterative processes are introduced, and the running time of the algorithm is also increased. Finally, the improvement of inertia weight attenuation function in PSO can improve the convergence speed of the algorithm and reduce the running time of the algorithm under the condition of ensuring the convergence accuracy of the evaluation algorithm. The experimental results show that the improved evaluation algorithm can reduce the running time of the algorithm under the condition of keeping the convergence accuracy.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：X824;TP183
【相似文獻】
相關期刊論文 前10條
1 謝先偉;;大規(guī)模煤炭數(shù)據(jù)下分布式神經網絡算法的研究與實現(xiàn)[J];煤炭技術;2013年09期
2 高淑芝;徐曉劍;王會;趙娜;;LMBP神經網絡算法的改進[J];沈陽化工大學學報;2014年01期
3 楊炳林,王良恩,黃詩煌;單一溶劑鹽溶液飽和蒸氣壓計算的新方法──改進的BP神經網絡算法[J];福建化工;2000年02期
4 肖立川,薛國新;BP神經網絡算法研究及其在燃煤鍋爐中應用[J];江蘇石油化工學院學報;2001年03期
5 孫開瓊,周云才;改進的神經網絡算法及其在油層識別中的應用[J];石油機械;2004年03期
6 周橋;高謙;;樣條權函數(shù)神經網絡算法在超前錨桿加固方式中的應用研究[J];金屬礦山;2009年11期
7 羅丹;李昌彩;吳長彬;;基于微粒群 BP神經網絡算法的堆石壩壩體變形監(jiān)控模型研究[J];巖石力學與工程學報;2012年S1期
8 張引沁;;利用神經網絡算法推算超額吉布斯自由能[J];新鄉(xiāng)學院學報(自然科學版);2008年04期
9 劉毅;張新;李艷;;基于虛擬儀器和神經網絡算法的旋轉機械故障診斷系統(tǒng)設計[J];煤礦機械;2011年11期
10 朱云;;計算機網絡連接增強優(yōu)化中的神經網絡算法[J];煤炭技術;2012年04期
相關會議論文 前10條
1 呂慶U，
本文編號：2320238
本文鏈接：http://sikaile.net/kejilunwen/huanjinggongchenglunwen/2320238.html
最近更新
教材專著
