Nonlinear System Identification using Least Squares Support Vector Machines
作者： Ming-guang Zhang , Xing-gui Wang , Wen-hui Li
DOI: 10.1109/ICNNB.2005.1614645
关键词:
摘要: Support vector machines (SVM) is a novel machine learning method based on small-sample statistical theory (SLT), and powerful for the problem with small sample, nonlinearity, high dimension, local minima. SVM have been very successful in pattern recognition, fault diagnoses function estimation problems. Least squares support (LS-SVM) an version which involves equality instead of inequality constraints works least cost function. This paper discusses algorithm introduces applications nonlinear control systems. Then identification MIMO models soft-sensor modeling proposed. The simulation results show that proposed provides tool has promising application industrial process
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