|
|||||
|
|
| Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network | |
| 引用本文： | 鄢田云,张翠芳,靳蕃. Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network[J]. 西南交通大学学报(英文版), 2003, 11(1): 9-15 |
| 作者姓名： | 鄢田云 张翠芳 靳蕃 |
| 作者单位： | School of Computer and Communication and Engineering，Southwest Jiaotong University，School of Computer and Communication and Engineering，Southwest Jiaotong University，School of Computer and Communication and Engineering，Southwest Jiaotong University Chengdu 610031，China，Chengdu 610031，China，Chengdu 610031，China |
| 摘 要： | Introduction Inpracticalapplication ,thereexistmoreorlessnonlinearphenomenainamajorityofphysicalsystems .Inordertostudythesekindsofphysicialsystems ,anonlinearsystemmodelmustbesetupfirstofall,andthensomeparametersidentificationofthepredefinedmodelmustb… |
| 关 键 词： | 遗传算法 动力学系统 RMNN 周期性多层神经网络 仿真 系统辨识 非线性系统 |
Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network |
|
| Yan Tianyun Zhang Cuifang Jin Fan. Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network[J]. Journal of Southwest Jiaotong University, 2003, 11(1): 9-15 | |
| Authors: | Yan Tianyun Zhang Cuifang Jin Fan |
| Abstract: | Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN). |
| Keywords: | genetic algorithm recurrent multilayer neural network identification simulation |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |