Title :
Water quality parameters prediction method based on Self-Organization Neural Network
Author :
Junfei, Qiao ; Yanmei, Jia ; Honggui, Han
Author_Institution :
Coll. of Electron. & Control Eng., Beijing Univ. of Technol., Beijing
Abstract :
According the problem of difficult to measure online water quality parameters of activated sludge process wastewater treatment system, this paper proposed a new growth self-organization neural network. This network can dynamic generate network nodes and grow to suitable network structure rapidly according to need in the learning process no need to advance set the value the structure and scale. The water quality parameters model of wastewater treatment system based on this network, have more strong adaptive ability, can learning online, network structure is simple, learning velocity rapid, prediction effluent water COD concentration effectively according to input, which proved high effectiveness of this method.
Keywords :
environmental science computing; self-organising feature maps; sludge treatment; wastewater treatment; activated sludge process wastewater treatment system; learning process; self-organization neural network; water quality parameters prediction method; Control engineering; Educational institutions; Electronic mail; Neural networks; Organizing; Prediction methods; Predictive models; Sludge treatment; Tellurium; Wastewater treatment; Neural Network; Self-Organization; modeling; wastewater treatment system; water quality prediction;
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
DOI :
10.1109/CCDC.2008.4597793