| 引用本文: | 陈杰,朱琳.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[J].控制理论与应用,2010,27(3):303~309.[点击复制] |
| CHEN Jie,ZHU Lin.New identification approach for nonlinear systems based on the combination network model of least squares and support vector machines[J].Control Theory and Technology,2010,27(3):303~309.[点击复制] |
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| 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 |
| New identification approach for nonlinear systems based on the combination network model of least squares and support vector machines |
| 摘要点击 1820 全文点击 1157 投稿时间：2008-06-12 修订日期：2009-05-10 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.3.CCTA080609 |
| 2010,27(3):303-309 |
| 中文关键词 混合专家系统 最小二乘支持向量机 非线性系统辨识 期望条件最大化 正则化 |
| 英文关键词 combination network model least squares and support vector machines nonlinear systems identification ECM regularization |
| 基金项目 北京市教育委员会共建重点实验室资助项目(CSYS100070417) . |
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| 中文摘要 |
| 针对基于输入输出数据的非线性系统辨识问题, 提出一种新的混合最小二乘支持向量机(LS-SVMs)网络模型及相应的学习算法. 该算法将系统的辨识问题动态自适应的划分为若干子问题, 将支持向量机(SVM)用于各子模块辨识; 通过分析模型的统计学特性, 给出基于整体框架优化的系统参数辨识方法. 针对系统中参数相关联的特性,采用期望条件最大化(ECM)算法对其进行条件辨识, 同时结合正则化理论和最小二乘法, 保证各专家模块的结构风险最小化辨识原则. 试验结果表明, 该方法兼具良好的辨识精度和泛化性能. |
| 英文摘要 |
| 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. |