Paper Info
Title
A fast online multivariable identification method for greenhouse environment control problems.
Abstract
Growing and pruning radial basis function (GAP-RBF) is extended for identification and control of multivariable nonlinear systems in this work. The proposed MGAP-RBF algorithm utilizes a sliding data window in the growing criterion and limits the number of hidden neurons by introducing a soft constraint in the pruning strategy to reduce the effect of disturbance and to improve learning speed, respectively. The performance of the proposed method is tested through some benchmark problems, and the results show that the proposed method can gain faster speed than the original GAP-RBF method and Ran algorithm, and more importantly, it can obtain an overwhelming advantages especially for some large-scale data sets with some complex attributes. Finally, the proposed method is applied to online PID tuning on a greenhouse environment control process. Simulation results show the proposed MGAP-RBF algorithm has better performance than the traditional RBF method and the original GAP-RBF method, in particular, it is faster and provides a more compact network with reduced computational complexity than the original GAP-RBF method.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.05.055
Neurocomputing
Keywords
Field
DocType
Growing and pruning radial basis Function (GAP-RBF),Online multivariable identification,Sequential learning algorithms,Greenhouse environment control,Proportional-integral-derivative
Data set,Radial basis function,Multivariable calculus,Multivariable nonlinear system,PID controller,Algorithm,Greenhouse,Artificial intelligence,Machine learning,Mathematics,Pruning,Computational complexity theory
Journal
Volume
ISSN
Citations 
312
0925-2312
1
PageRank 
References 
Authors
0.35
23
6
Name
Order
Citations
PageRank
Haigen Hu1448.47
Cheng Luo2297.86
Qiu Guan383.49
Xiao-Xin Li482.80
Sheng-Yong Chen51077114.06
Qianwei Zhou6456.53