| 引用本文: | 胡德文,王正志,周宗潭.基于神经网络的非线性控制系统自组织辨识.[J].国防科技大学学报,1998,20(2):85-90.[点击复制] |
| Hu Dewen,Wang Zhengzhi,Zhou Zhongtan.Self-organizing Identification of Nonlinear Control Systems Based on Neural Networks[J].Journal of National University of Defense Technology,1998,20(2):85-90[点击复制] |
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| 基于神经网络的非线性控制系统自组织辨识 |
| 胡德文, 王正志, 周宗潭 |
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(国防科技大学 自动控制系 湖南 长沙 410073)
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| 摘要: |
| 本文首先将自组织神经网络算法向一般化情形引伸, 接着把自组织过程应用到一般非线性系统的动态过程分类, 使得整个非线性系统能够按照输入输出样本空间的概率密度自组织, 成为许多具有不同分类核心和感受野的线性子空间逼近。在此基础上, 我们采用通用最小二乘算法, 以子空间的非线性问题线性化误差作为依据, 并进一步运用自组织神经网络的合作与竞争思想, 最终得到一般情形的非线性系统的最小二乘辨识。仿真结果表明了本方法的可行性与优越性。 |
| 关键词: 非线性系统, 神经网络, 最小二乘算法, 自组织算法, 系统辨识 |
| DOI： |
| 投稿日期：1998-02-18 |
| 基金项目:国家自然科学基金, 湖南省自然科学基金资助 |
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| Self-organizing Identification of Nonlinear Control Systems Based on Neural Networks |
| Hu Dewen, Wang Zhengzhi, Zhou Zhongtan |
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(Department of Automatic Control, NUDT, Changsha, 410073)
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| Abstract: |
| This paper firstly extends the self-organizing neural networks to general case. Then the self-organizing process is applied to classify the dynamic process of nonlinear control systems. The nonlinear system is self-organized according to the probability density of the input and output samples and is approximated by many linear sub-spaces with different classifying centers and receptive fields. The self-organizing least squares identification of nonlinear systems is constructed based on the general least squares algorithms, the linearization errors of sub-spaces, and the cooperation and competition mechanism. The simulation results have shown the efficiency of the suggested algorithm. |
| Keywords: Nonlinear systems, neural networks, least squares algorithm, self-organizing, system identification |
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