.png){kind=icon}
返回
Parametric system identification using neural networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.05.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Neural networks are used in many applications such as image recognition, classification, control and system identification. However, the parameters of the identified system are embedded within the neural network architecture and are not identified explicitly. In this paper, a mathematical relationship between the network weights and the transfer function parameters is derived. Furthermore, an easy-to-follow algorithm that can estimate the transfer function models for multi-layer feedforward neural networks is proposed. These estimated models provide an insight into the system dynamics, where information such as time response, frequency response, and pole/zero locations can be calculated and analyzed. In order to validate the suitability and accuracy of the proposed algorithm, four different simulation examples are provided and analyzed for three-layer neural network models. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Neural networks
Transfer functions
System identification
System response
ARMA models
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Robust on-line nonlinear systems identification using multilayer dynamic neural networks with two-time scales.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏On-line identification of nonlinear systems using Volterra polynomial basis function neural networksNEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏On-line identification and reconstruction of finite automata with generalized recurrent neural networksNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
查看更多内容