本文選題：污水處理 + 極限學(xué)習(xí)機(jī)��； 參考：《化工學(xué)報(bào)》2016年06期

【摘要】：污水處理過程容易受外界沖激擾動(dòng)影響,引發(fā)污泥上浮、老化、中毒、膨脹等故障工況,導(dǎo)致出水水質(zhì)質(zhì)量差,能源消耗高等問題,如何快速準(zhǔn)確識(shí)別污水操作工況故障至關(guān)重要。針對(duì)污水工況識(shí)別過程中現(xiàn)有監(jiān)督學(xué)習(xí)方法未利用大量未標(biāo)記數(shù)據(jù)蘊(yùn)含的豐富操作工況信息,采用基于流形正則化極限學(xué)習(xí)機(jī)的半監(jiān)督學(xué)習(xí)方法,監(jiān)視生化污水處理過程操作運(yùn)行工況。該方法在學(xué)習(xí)過程中,在標(biāo)記和未標(biāo)記數(shù)據(jù)輸入空間構(gòu)建圖拉普拉斯算子,通過隨機(jī)特征映射建立隱含層,在流形正則化框架下,求解隱含層和輸出層之間的權(quán)重,保留隨機(jī)神經(jīng)網(wǎng)絡(luò)的計(jì)算效率和泛化性能。仿真實(shí)驗(yàn)結(jié)果表明,基于半監(jiān)督極限學(xué)習(xí)機(jī)的污水處理工況識(shí)別在準(zhǔn)確率與可靠性方面相對(duì)優(yōu)于基本極限學(xué)習(xí)機(jī)方法。
[Abstract]:The process of sewage treatment is easily affected by the disturbance of external impulse, causing problems such as sludge floating, aging, poisoning, swelling and other malfunction conditions, resulting in poor quality of effluent quality, high energy consumption and so on. It is very important to identify the fault of sewage operation condition quickly and accurately. Based on manifold regularization limit learning machine, a semi-supervised learning method based on manifold regularization is proposed to solve the problem that the existing supervised learning methods do not utilize the abundant operating condition information contained in a large number of unlabeled data in the process of sewage condition identification. Monitor the operation and operation of biochemical wastewater treatment process. In the process of learning, the graph Laplace operator is constructed in the labeled and unmarked data input space, the hidden layer is established by random feature mapping, and the weights between the hidden layer and the output layer are solved under the framework of manifold regularization. The computational efficiency and generalization performance of preserving stochastic neural networks. Simulation results show that the recognition of sewage treatment conditions based on semi-supervised extreme learning machine is better than that of basic extreme learning machine in terms of accuracy and reliability.
【作者單位】： 沈陽(yáng)化工大學(xué)信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61203102,61573364) 遼寧省教育廳科學(xué)研究項(xiàng)目(L2013158,L2013272)~~
【分類號(hào)】：X703
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本文編號(hào)：2008000