%0 Journal Article
%T New identification approach for nonlinear systems based on the combination network model of least squares and support vector machines<br>基于混合最小二乘支持向量机网络模型的非线性系统辨识
%A CHEN Jie
%A ZHU Lin
%A <br>陈杰
%A 朱琳
%J 控制理论与应用
%D 2010
%I 
%X A novel combination network model of least squares and support vector machines(MLS-SVMs) and the associate learning algorithm for identifying nonlinear systems based on the input-output data are proposed. In the model, the identification task is dynamically decomposed into several subtasks according to the physical or statistical natures of the problem. The SVMs are applied as learning machines to every subtask. After analyzing the statistical characteristics of the model in the formal characterization, we give an algorithm for training the MLS-SVMs, based on the frame optimizing principle. The expectation conditional maximization(ECM) algorithm is applied to solve the dependence problem of parameters. Regularization theory and least squares method assure the identification principle of minimal construction risk for expert modules. Experiment illustrates good performance of the proposed method by high approximation accuracy and generalization levels.
%K combination network model
%K least squares and support vector machines
%K nonlinear systems identification
%K ECM
%K regularization<br>混合专家系统
%K 最小二乘支持向量机
%K 非线性系统辨识
%K 期望条件最大化
%K 正则化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=7EC2F8956BEA0835505D0C0930FD7FAA&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=358F98408588E522&eid=F637763636425CAF&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=15