Title :
Nonlinear identification based on least squares support vector machine
Author :
Li, Haisheng ; Zhu, Xuefeng ; Shi, Bubai
Author_Institution :
Coll. of Inf. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
Least squares support vector machine LS-S is one of the S methods which can overcome the dimension disaster of the classic quadratic program method to train the support vector machine, it is fit for the training of large scale data, this paper uses LS-S to model a classic nonlinear system, continue stirred and reactor CSTR. The simulation is taken to demonstrate correctness and effectiveness of the proposed approach.
Keywords :
identification; least squares approximations; nonlinear systems; support vector machines; CSTR; continue stirred and reactor; dimension disaster; least squares support vector machine; nonlinear identification; quadratic program method; system identification; Artificial neural networks; Autoregressive processes; Educational institutions; Large-scale systems; Least squares methods; Neural networks; Nonlinear systems; Polynomials; Support vector machine classification; Support vector machines;
Conference_Titel :
Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th
Print_ISBN :
0-7803-8653-1
DOI :
10.1109/ICARCV.2004.1469796
