Title :
RBF neural network predictive control for coagulant dosage
Author_Institution :
Coll. of Autom., Nanjing Univ. of Technol., China
Abstract :
Coagulant control plays an important role in water treatment process. Considering the nonlinear and time-delaying property of coagulation, a model of coagulant dosage based on RBF neural network is developed. The Gaussian function is used for hidden note function, whose centers are adjusted by K-means clustering algorithm. The weights of output layer are obtained based on RLS. The model has been trained and checked by practical data of water plant. The results verify the feasibility of the proposed approach. With optimal algorithm, a closed-loop predictive control system can be established to realize the real time control of optimal coagulant dose rate.
Keywords :
Gaussian processes; closed loop systems; coagulation; delays; least squares approximations; neurocontrollers; nonlinear control systems; pattern clustering; predictive control; radial basis function networks; recursive estimation; water treatment; Gaussian function; K-means clustering algorithm; RBF neural network predictive control; RLS method; closed loop predictive control system; coagulant dosage control; coagulation; nonlinear delaying property; nonlinear property; radial basis function; real time control; recursive least squares method; time delaying property; water treatment process; Automatic control; Clustering algorithms; Coagulation; Educational institutions; Electronic mail; Neural networks; Prediction algorithms; Predictive control; Real time systems; Resonance light scattering;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1342068
