.png){kind=icon}
返回
Volterra system identification using adaptive genetic algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2004.05.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper a floating point genetic algorithm (GA) for Volterra system identification is presented. The adaptive GA method suggested here addresses the problem of determining the proper Volterra candidates which leads to the smallest error between the identified nonlinear system and the Volterra model. This is achieved by using variable length GA chromosomes which encode the coefficients of the selected candidates. During the process of evolution the candidates with the least significant contribution in the error reduction process is removed. The proposed GA method detects the proper Volterra candidates and the associated coefficients in one single evolutionary process. The fitness function employed by the algorithm prevents irrelevant candidates from taking part of the final solution. Genetic operators are chosen to suit the floating point representation of the genetic data. As the evolution process improves and the method reaches a near-global solution, a local search is implicitly applied by zooming in the search interval of each gene by adaptively changing the boundaries of those intervals. The proposed algorithms has produced excellent results in modeling different nonlinear systems. (C) 2004 Elsevier B.V. All rights reserved.
Keyword:
evolutionary computation
system identification
Volterra systems
polynomial approximation
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
暂无机构信息
引用论文
Laser-Induced Breakdown Spectroscopy (LIBS) Analysis of Calcium Ions Dissolved in Water Using Filter Paper Substrates: An Ideal Internal Standard for Precision Improvement使用滤纸基底对溶解在水中的钙离子进行激光诱导击穿光谱 (LIBS) 分析: 提高精度的理想内标
.png){kind=icon}
PREAI
分享收藏分享收藏
分享收藏
查看更多内容