| 引用本文: | 钟伟民, 皮道映, 孙优贤.基于支持向量机的直接逆模型辨识[J].控制理论与应用,2005,22(2):307~310.[点击复制] |
| ZHONG Wei-min, PI Dao-ying, SUN You-xian.Support vector machine based direct inverse-model identification[J].Control Theory and Technology,2005,22(2):307~310.[点击复制] |
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| 基于支持向量机的直接逆模型辨识 |
| Support vector machine based direct inverse-model identification |
| 摘要点击 1871 全文点击 2631 投稿时间：2003-06-24 修订日期：2004-05-08 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.2.027 |
| 2005,22(2):307-310 |
| 中文关键词 逆模型 支持向量机(SVM) BP神经网络 建模与辨识 |
| 英文关键词 inverse-model support vector machine(SVM) BP neural network modeling and identification |
| 基金项目 国家973项目资助(2002CB312200). |
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| 中文摘要 |
| 在简单讨论逆模型辨识原理的基础上,利用支持向量机(SVM)对函数逼近的能力,提出了基于支持向量机的直接逆模型辨识方法.分别采用二次核函数以及高斯RBF核函数,利用训练数据对线性和非线性系统进行黑箱辨识.仿真结果表明,基于支持向量机的直接逆模型辨识方法在处理线性和非线性对象时,辨识性能都优于传统的BP神经网络,不仅辨识精度高,辨识速度快,而且泛化能力较强. |
| 英文摘要 |
| After a simple discussion of the principle of the inverse_model identification,a support vector machines(SVM) based direct inverse-model identification method is developed by using SVM's excellent ability of function approximation.According to the train data,linear and nonlinear systems' black-box identification is performed by using SVM with quadric polynomial and Gaussian RBF kernel respectively.Simulation results show that the performance of SVM based direct inverse-model is better than that of BP neural network in that it has better identification precision,quicker identification speed and stronger generalization ability. |
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