Title :
A new input selection method for neural modeling of nonlinear complex systems
Author_Institution :
Sch. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
Abstract :
In neural modeling of non-linear dynamic systems, the neural inputs may include any system inputs of interest and system outputs with various time delays. To obtain the optimal subset of inputs regarding a performance measure is a combinational problem, and the selection process can be time-consuming. In this paper, the input selection is transformed to a model structure selection problem and a new input selection method is proposed. This method is then applied to the neural modeling of a practical system, and the modeling result shows its merit.
Keywords :
delays; large-scale systems; neural nets; nonlinear dynamical systems; pollution control; polynomials; regression analysis; steam power stations; NOx emission control; coal fired power plant; input selection method; model structure selection; neural modeling; nonlinear complex systems; nonlinear dynamic systems; polynomial regression model; time delays; Delay effects; Function approximation; Input variables; Intelligent control; Least squares approximation; Least squares methods; Neural networks; Nonlinear dynamical systems; Polynomials; Power system modeling;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1341936
