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使用布谷鸟搜索算法提取T-S模糊模型

Extracting T-S Fuzzy Models Using the Cuckoo Search Algorithm.

作者信息

Turki Mourad, Sakly Anis

机构信息

Research Unit of Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), 5019 Monastir, Tunisia.

出版信息

Comput Intell Neurosci. 2017;2017:8942394. doi: 10.1155/2017/8942394. Epub 2017 Jul 6.

DOI:10.1155/2017/8942394
PMID:28761439
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5518498/
Abstract

A new method called cuckoo search (CS) is used to extract and learn the Takagi-Sugeno (T-S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T-S fuzzy model. These parameters are learned simultaneously. The optimized T-S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box-Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods. The proposed CS method gives an optimal T-S fuzzy model with fewer numbers of rules.

摘要

一种名为布谷鸟搜索（CS）的新方法被用于提取和学习高木-关野（T-S）模糊模型。在所提出的方法中，CS的粒子或布谷鸟由T-S模糊模型的规则结构（包括规则数量和所选规则）、前提和结论参数构成。这些参数同时进行学习。通过三个例子对优化后的T-S模糊模型进行验证：第一个是一个非线性对象建模问题，第二个是一个Box-Jenkins非线性系统辨识问题，第三个是非线性系统的辨识，将所得结果与其他方法的现有结果进行比较。所提出的CS方法给出了具有更少规则数量的最优T-S模糊模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/1ca4f63359bd/CIN2017-8942394.pseudo.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/c9a764088507/CIN2017-8942394.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/6d37bfa5669f/CIN2017-8942394.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/543dd4407b5a/CIN2017-8942394.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/7d20cf9eb83b/CIN2017-8942394.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/35cacf6497fd/CIN2017-8942394.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/1ca4f63359bd/CIN2017-8942394.pseudo.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/c9a764088507/CIN2017-8942394.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/6d37bfa5669f/CIN2017-8942394.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/543dd4407b5a/CIN2017-8942394.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/ea1232e2b2f1/CIN2017-8942394.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/2f92a0bbbb2a/CIN2017-8942394.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/cc6e14455f8a/CIN2017-8942394.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/7d20cf9eb83b/CIN2017-8942394.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/35cacf6497fd/CIN2017-8942394.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d8/5518498/1ca4f63359bd/CIN2017-8942394.pseudo.001.jpg

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本文引用的文献

1
Fault Estimation Observer Design for Discrete-Time Takagi-Sugeno Fuzzy Systems Based on Homogenous Polynomially Parameter-Dependent Lyapunov Functions.基于齐次多项式依赖 Lyapunov 函数的离散时间 Takagi-Sugeno 模糊系统故障估计观测器设计。
IEEE Trans Cybern. 2017 Sep;47(9):2504-2513. doi: 10.1109/TCYB.2017.2693323. Epub 2017 Apr 20.
2
Further studies on control synthesis of discrete-time T-S fuzzy systems via augmented multi-indexed matrix approach.基于增广多指标矩阵方法的离散时间 T-S 模糊系统控制综合的进一步研究。
IEEE Trans Cybern. 2014 Dec;44(12):2784-91. doi: 10.1109/TCYB.2014.2316491. Epub 2014 Apr 29.
3
Identification and control of dynamical systems using neural networks.
利用神经网络对动态系统进行识别与控制。
IEEE Trans Neural Netw. 1990;1(1):4-27. doi: 10.1109/72.80202.
4
A two-stage evolutionary process for designing TSK fuzzy rule-based systems.一种用于设计基于TSK模糊规则系统的两阶段进化过程。
IEEE Trans Syst Man Cybern B Cybern. 1999;29(6):703-15. doi: 10.1109/3477.809026.
5
Neuro-fuzzy rule generation: survey in soft computing framework.神经模糊规则生成：软计算框架中的综述
IEEE Trans Neural Netw. 2000;11(3):748-68. doi: 10.1109/72.846746.
6
Evolving compact and interpretable Takagi-Sugeno fuzzy models with a new encoding scheme.采用新编码方案的紧凑且可解释的进化Takagi-Sugeno模糊模型。
IEEE Trans Syst Man Cybern B Cybern. 2006 Oct;36(5):1006-23. doi: 10.1109/tsmcb.2006.872265.
7
An approach to online identification of Takagi-Sugeno fuzzy models.一种用于在线辨识Takagi-Sugeno模糊模型的方法。
IEEE Trans Syst Man Cybern B Cybern. 2004 Feb;34(1):484-98. doi: 10.1109/tsmcb.2003.817053.