SIA OpenIR  > 信息服务与智能控制技术研究室
Alternative TitleSelective ensemble simulate meta-model based-on global optimize strategy
汤健; 李东; 郑文荣; 丛秋梅; 刘卓
Conference Name11th World Congress on Intelligent Control and Automation (WCICA 2014)
Conference DateJune 29 - July 4, 2014
Conference PlaceShenyang, China
Source PublicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
Publication PlacePiscataway, NJ, USA
Indexed ByEI ; CPCI(ISTP)
EI Accession number20152600977760
WOS IDWOS:000393066200163
Contribution Rank3
Keyword复杂系统建模 分析仿真 仿真元建模 选择性集成 基于随机权重的单隐层前馈网络
Abstract模型复杂度和规模的不断增加已经成为提高模拟系统分析仿真效率和高层决策者认知复杂系统行为的瓶颈之一。采用仿真元模型替代复杂物理模型是解决该问题的有效方法。针对基于传统神经网络等机器学习的仿真元建模方法建模速度慢、难以有效进行模型更新,以及单一模型仿真元建模方法精度低、泛化性差等问题,本文提出了基于全局优化的选择集成建模策略,并将随机权重单隐层前馈网络(SLFNrw)用于建立仿真元建模。本文首先对复杂系统模拟中的仿真元模型技术进行了分析,然后给出了基于全局优化策略选择集成SLFNrw 的仿真元建模方法,最后采用合成数据和Benchmark 数据进行了算法测试。仿真结果表明该方法可以在建模精度和速度间获得较佳的均衡,在基于仿真的复杂系统分析中具有广阔的应用前景。
Other AbstractThe increment of model complexity and size has been bottle neck of improve simulation system analyze emulation effective and decision maker cognize complex system. One of the effective methods to solve this problem is to replace complex physical model with simple simulate meta-model. Aim at slowly modeling speed and difficulty to effective updating problem using traditional neural network and other machine learning based simulate meta-model algorithm, and lower modeling accurate and generalization et al problems, a new global optimization based selective ensemble strategy is proposed in this paper, and single-hidden layer feed-forward networks with random weights (SLFNrw) is used to construct simulate meta-model. At first, simulate meta-modeling technology using in complex system simulation is analyzed. Then, global optimization based selective ensemble SLFNrw simulate meta-modeling strategy and algorithm are clarified in detail. At last, synthetic function and benchmark data are used to test the proposed algorithm. The results show the proposed algorithm can obtain well trade-off between modeling accuracy and speed, which can be widely used in complex system analysis based on simulation.
Citation statistics
Document Type会议论文
Affiliation1.Research Institute of Computing Technology, Beijing Jiaotong University, Beijing, China
2.College of Electronic Engineering, Naval University of Engineering, Wuhan, China
3.Department of Information Service and Intelligent Control, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China
4.Research Center of Automation, Northeastern University, Shenyang, China
Recommended Citation
GB/T 7714
汤健,李东,郑文荣,等. 基于全局优化策略选择集成仿真元模型[C]. Piscataway, NJ, USA:IEEE,2014:922-927.
Files in This Item:
File Name/Size DocType Version Access License
基于全局优化策略选择集成仿真元模型.pd(813KB)会议论文 开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[汤健]'s Articles
[李东]'s Articles
[郑文荣]'s Articles
Baidu academic
Similar articles in Baidu academic
[汤健]'s Articles
[李东]'s Articles
[郑文荣]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[汤健]'s Articles
[李东]'s Articles
[郑文荣]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 基于全局优化策略选择集成仿真元模型.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.