Prediction Of Sewage Quality Based On Fusion Of Bpnetworks

[1]Vapnik, Vladimir N. Controlling the Generalization Ability of Learning Processes. TheNature of Statistical Learning Theory. Springer New York, 2000:89-118.10.1007/978-1-4757-2440-0_5 Search in Google Scholar

[2]V.Vapnik. The nature of statistical Learning Theory, New York: Springer-Verlag,1999.10.1007/978-1-4757-3264-1 Search in Google Scholar

[3]Bailey, Max, et al. “Hybrid Systems for Prediction - A Case Study of Predicting Effluent Flow to a Sewage Plant.” annes IEEE Computer Society, 1995:261. Search in Google Scholar

[4]Tian, Null Jingwen, N. M. Gao, and N. Y. Xiang. “Intelligent Optimized Control of Flocculation Process of Sewage Treatment Based on Support Vector Machine.” Information Acquisition, 2006 IEEE International Conference on IEEE, 2006:1487-1491.10.1109/ICIA.2006.305977 Search in Google Scholar

[5]Chen, W. C., N. B. Chang, and J. C. Chen. “Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment.” Water Research 37.1(2003):95-107.10.1016/S0043-1354(02)00255-5 Search in Google Scholar

[6]Wang, Wen Cheng, et al. “Soft Measurement Technique of Sewage Treatment Parameters Based on Wavelet Neural Networks.” Applied Mechanics & Materials 556-562(2014):3168-3171.10.4028/www.scientific.net/AMM.556-562.3168 Search in Google Scholar

[7]Côté, P., et al. “Energy Consumption of MBR for Municipal Wastewater Treatment: Current Situation and Potential.” Forest Engineering (2013).10.2175/193864713813503297 Search in Google Scholar

[8]Wang, M. H., et al. “Mathematical modeling of electrical energy consumption and heating requirements by municipal wastewater treatment plants.” J. Environ. Sci.; (United States) 22:4.4(1979):23-26. Search in Google Scholar

[9]OLSSON G, ANDREWS J F. “Dissolved oxygen control in the activated sludge process.” Wat. Sci. Tech., 1981,13(10):341-347. Search in Google Scholar

[10]CHARPENTIER J, FLORENTZ M, DAVID G. “Oxidation-reduction potential (ORP) regulation: a way to optimize pollution removal and energy savings in the low load activated sludge process.” Wat. Sci. Tech., 1987,19(Rio):645-655.10.2166/wst.1987.0244 Search in Google Scholar

[11]Thevenot, D. R. “Oxidation Reduction Potential (Orp) Regulation as a Way to Optimize Aeration and C, N and P Removal - Experimental Basis and Various Full-Scale Examples - Discussion.” Waterence & Technology 21.12(1989):1622-1623. Search in Google Scholar

[12]Ferrer, J., et al. “Energy saving in the aeration process by fuzzy logic control.” Water Science & Technology 38.3(1998):209-217.10.2166/wst.1998.0210 Search in Google Scholar

[13]Bongards, M., A. Ebel, and T. Hilmer. “Predictive control of wastewater works by neural networks.” Automation Congress, 2004. Proceedings. World 2004:397 - 402. Search in Google Scholar

[14]Yuzhao, F., et al. “Optimal parameters of activated sludge system robust control method” China Water & Wastewater 19.3(2003):14-16. Search in Google Scholar

[15]Tzu-Yi, Pai, et al. “Predicting the co-melting temperatures of municipal solid waste incinerator fly ash and sewage sludge ash using grey model and neural network..” Waste Manag Res 29.3(2011):284-93.10.1177/0734242X1036786220406756 Search in Google Scholar

[16]Huang, Y. W., and M. Q. Chen. “Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge.” Measurement73(2015):640-648.10.1016/j.measurement.2015.06.014 Search in Google Scholar

[17]Jeong, Hyeong Seok, S. H. Lee, and H. S. Shin. “Feasibility of On-line Measurement of Sewage Components Using the UV Absorbance and the Neural Network.” Environmental Monitoring & Assessment133.11(2007):15-24.10.1007/s10661-006-9555-417286182 Search in Google Scholar

[18]Sadeghi, R., et al. “Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran.” Ecological Modelling244.1745(2012):117-126.10.1016/j.ecolmodel.2012.06.029 Search in Google Scholar

[19]Olsson G. [Sweden]. Wastewater Treatment Systems modeling,diagnosis and control.Chemical Industry Press, 2005. Search in Google Scholar