| 基于遗传算法的动态神经网络的建模与应用 |
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| 引用本文： | 王建南,段慧达.基于遗传算法的动态神经网络的建模与应用[J].计算技术与自动化,2004,23(1):37-39. |
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| 作者姓名： | 王建南 段慧达 |
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| 作者单位： | 北华大学,电气信息学院,吉林,132021 |
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| 摘 要： | 本文分析了改进的ELMAN网络的结构，并讨论了神经网络的学习算法，针对BP算法的缺陷，提出了用遗传算法修正网络权值的学习算法。另外，将采用遗传算法进行训练的改进ELMAN网络应用于非线性系统的辨识和建模。通过仿真和在汽车磷化加热系统建模中的应用进一步说明了该方法用于高阶次非线性系统建模的可行性。
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| 关 键 词： | 遗传算法 动态神经网络 ELMAN网络 BP算法 网络权值 汽车磷化加热系统 系统辨识 |
| 文章编号： | 1003-6199(2004)01-0037-03 |
| 修稿时间： | 2003年7月23日 |
Modeling and Application Based on Dynamic Recurrent Neural Network Trained with Genetic Algorithm |
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| WANG Jian-nan,DUAN Hui-da.Modeling and Application Based on Dynamic Recurrent Neural Network Trained with Genetic Algorithm[J].Computing Technology and Automation,2004,23(1):37-39. |
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| Authors: | WANG Jian-nan DUAN Hui-da |
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| Abstract: | A modified ELMAN network and its algorithm are studied in this paper. To overcome the slow convergence of the BP algorithm, genetic algorithm (GA) is proposed, which can train the weight of the network. In addition, a given model is identified by using modified ELMAN network trained with GA, and the model of phosphating temperature control system is also established by this method. Both simulation and experiment demonstrate the effectiveness of the proposed algorithm in system modeling. |
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| Keywords: | modified ELMAN network genetic algorithm(GA) system identification phosphating |
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