模糊神经网络用于非线性系统模型辨识
- 1. 西南交通大学计算机与通信工程学院 成都 610031
作者简介:
翟东海(1974-),男,博士生,主要从事神经网络、模糊推理、组合优化方面的研究;李力(1974-),男,博士生,主要从事神经网络、模糊推理、数据挖掘方面的研究;靳番(1934-),男,教授,博士生导师,主要从事编码、组合优化、人工神经网络、模糊推理方面的研究.
翟东海(1974-),男,博士生,主要从事神经网络、模糊推理、组合优化方面的研究;李力(1974-),男,博士生,主要从事神经网络、模糊推理、数据挖掘方面的研究;靳番(1934-),男,教授,博士生导师,主要从事编码、组合优化、人工神经网络、模糊推理方面的研究.
- 收稿日期:
2003-03-13
- 刊出日期:
2004-10-15
摘要: 提出了一种非线性系统的模型辨识方法。在只有被辨识系统的输入输出数据的情况下,利用一种无监督的聚类算法来进行结构辨识,从而自动获得模糊规则库,并可以得到模糊系统的初始参数。在聚类的基础上,构造一个与之相匹配的模糊神经网络,用它的学习算法来训练网络得到一个精确的模糊模型,从而实现参数辨识。同时,证明了所构造的模糊神经网络具有通用逼近能力,这个能力在模糊建模和模糊控制方面非常有用。通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。
Nonlinear-Systems Model Identification with AdditiveMultiplicative Fuzzy Neural Network
- 1. School of Computer and Communication Engineering,Southwest Jiaotong University Chengdu 610031
- Received Date:
2003-03-13
- Publish Date:
2004-10-15
Abstract: A model identification approach of nonlinear systems where only the input-output data of the identified system are available is presented. To automatically acquire the fuzzy rule-base and the initial parameters of the fuzzy model, an unsupervised clustering method is used in structure identification. Based on the cluster result, a Fuzzy Neural Network (FNN) is constructed to match with it. The FNN is trained by its learning algorithm to obtain a precise fuzzy model and realize parameter identification. Finally, the effectiveness of the proposed technique is confirmed by the simulation results of two nonlinear systems.
翟东海, 李力, 靳蕃. 模糊神经网络用于非线性系统模型辨识[J]. 电子科技大学学报, 2004, 33(5): 577-581.
| 引用本文: |
翟东海, 李力, 靳蕃. 模糊神经网络用于非线性系统模型辨识[J]. 电子科技大学学报, 2004, 33(5): 577-581.
|
Zhai Donghai, Li Li, Jin Fan. Nonlinear-Systems Model Identification with AdditiveMultiplicative Fuzzy Neural Network[J]. Journal of University of Electronic Science and Technology of China, 2004, 33(5): 577-581.
| Citation: |
Zhai Donghai, Li Li, Jin Fan. Nonlinear-Systems Model Identification with AdditiveMultiplicative Fuzzy Neural Network[J]. Journal of University of Electronic Science and Technology of China, 2004, 33(5): 577-581.
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