Huang Daoping (黄道平), Liu Yiqi (刘乙奇), Li Yan (李艳). Research and application of soft measurement in the sewage treatment process [J]. CIESC Journal (化工学报), 2011,62 (1): 56-64.
[2]
Guo Nan (郭楠), Qiao Junfei (乔俊飞). Research of BOD soft measuring instrument based on neural network [D]. Beijing: Beijing University of Technology, 2014.
[3]
Chen Zhaobo (陈兆波), Ren Yueming (任月明). Sewage Treatment Plant Measurement, Automatic Control and Fault Diagnosis (污水处理厂测量、自动控制与故障诊断) [M]. Beijing: Chemical Industry Press, 2009, 16-18.
[4]
Tian Yi (田奕), Qiao Junfei (乔俊飞). Neural network soft measurement of BOD based on genetic algorithm [J]. Computer Technology and Development (计算机技术与发展), 2009, 19 (3): 127-133.
[5]
Li Guihong (李贵宏), Zheng Hua (郑华). Application of artificial neural net work in wastewater treatment//Second International Conference on Information Science and Engineering, ICISE 2010 [C]. Hangzhou, 2010: 4373-4375.
[6]
Chen Zhiming (陈志明). Wastewater treatment prediction based on chao GA optimization LS-SVM//Proceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011 [C]. Guizhou, 2011: 4013-4016.
[7]
Yang Baolei (杨鲍蕾). Prediction system of sewage outflow COD based on LS-SVM//Proceedings of the Second International Conference on Intelligent Control and Information Processing, ICICIP 2011 [C]. Hangzhou, 2011: 399-402.
[8]
Ran Weili (冉维丽), Qiao Junfei (乔俊飞). BOD soft-measuring approach based on PCA time-delay neural network [J]. Transaction of China Electrotechnical Society (电工技术学报), 2004, 19 (12): 78-82.
[9]
Su Shuhui (苏书惠), Zhang Shaode (张绍德), Tan Jinghui (谭敬辉). Research of waste water soft-measuring approach based on support vector machine [J]. Automation & Instrumentation (自动化与仪表), 2009, (6): 6-9.
[10]
Pani A K, Mohanta H K. Application of support vector regression, fuzzy inference and adaptive neuro fuzzy inference techniques for online monitoring of cement fitness. [J]. Powder Technology, 2014, 264: 484-497.
[11]
Tipping M E. Sparse Bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001, 1 (3): 211-244.
[12]
Michael E Tipping, Anita Faul. Fast marginal likelihood maximization for sparse Bayesian models//Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics [C]. Key West, 2003.
[13]
Xu Jiping (许继平), Chen Chen (陈晨), Liu Zaiwen (刘载文), Wang Xiaoyi (王小艺). Research on BOD online detection instrument based on the theory of soft instrument [J]. Control Engineering of China (控制工程), 2010, 17: 101-108.
[14]
Zhang Xiuju (张秀菊), An Huan (安焕), Zhao Wenrong (赵文荣), Zhang Qinling (张琴玲). Application of waste water prediction based on support vector machine [J]. Chinese Rural Water Conservancy and Hydroelectric Power (中国农村水利水电), 2015, (1): 85-89.
[15]
Xu Yuge (许玉格), Cao Tao (曹涛), Luo Fei (罗飞). The prediction of effluent quality of waste water treatment based on relevance vector machine [J]. Journal of South China University of Technology: Natural Science Edition (华南理工大学学报: 自然科学版), 2014, 42 (5): 111-117.
[16]
Wang Huazhong (王华忠), Yu Jinshou (俞金寿). Research on kernel function and its application in soft measurement modeling [J]. Automation & Instrumentation (自动化与仪表), 2004, 25 (10): 22-25.
[17]
Masuda Kazuaki. Global optimization of point search by equilibrium search of gradient dynamical system [J]. Electronic and Communication in Japan, 2008, 91 (1): 19-31.
[18]
Su Jieqiong, Wang Xuan, Liang Yong. GA-based support vector machine model for the predictor for the monthly reservoir storage [J]. Journal of Hydrologic Engineering, 2014, 19: 1430-1437.
[19]
Thomas Buchgraber, Dmitriy Shutin, Vincent Poor H. A sliding-window online fast variable sparse Bayesian learning algorithm//2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) [C]. 2011: 2128-2133.