Title :
Data reconciliation of nonlinear dynamic process based on LSSVM
Author :
Zhang, Xiufang ; Fu, Kechang ; Zhou, Rong
Author_Institution :
Dept. of Control Eng., Chengdu Univ. of Inf. Technol., Chengdu, China
Abstract :
A new data reconciliation algorithm based on least squares support vector machines (LSSVM) for nonlinear dynamic process is proposed in this work. Firstly, response of processes and training data is obtained by computation tools or simulation software. Secondly, the local models of processes are identified by LSSVM. Finally, process data reconciliation is transformed to nonlinear program problem with constraint equation. Compare to the prevailing data reconciliation method for nonlinear dynamic processes- NDDR. The proposed algorithm is simple, easy to realize. In addition, the mechanism model does not be involved, which is substituted by the LSSVM model identified by sampling data online. Simulation results demonstrate the superiority of the proposed algorithm.
Keywords :
data handling; digital simulation; least squares approximations; support vector machines; LSSVM; computation tools; data reconciliation; least squares support vector machines; nonlinear dynamic process; simulation software; Computational modeling; Control engineering; Information technology; Least squares methods; MIMO; Nonlinear equations; Sampling methods; Software tools; Support vector machines; Training data; LSSVM; data reconciliation; nonlinear dynamic processes; systen identification;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7737-1
DOI :
10.1109/MACE.2010.5535843
