research-article

Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learning

Authors:

Xian-Xia Zhang,

Lian-rong Zhao,

Bing WangAuthors Info & Claims

Applied Soft Computing, Volume 59, Issue C

Pages 378 - 388

https://doi.org/10.1016/j.asoc.2017.04.064

Published: 01 October 2017 Publication History

Abstract

We decompose complex spatially distributed systems with multiple control.Sources into multiple sub-systems with one control source.Space-decomposition based 3D fuzzy control scheme is proposed.A data-driven multiple 3D FLC design method is developed.Multiple single-output SVRs with spatial kernel functions are presented to cope with a multi-output spatio-temporal data set. Three-dimensional fuzzy logic controller (3D FLC) is a recently developed FLC integrating space information expression and processing for nonlinear spatially distributed dynamical systems (SDDSs). Like a traditional FLC, expert knowledge can help design a 3D FLC. Nevertheless, there are some situations where expert knowledge cannot be formulated into precise words; what's worse, it might not be explicitly expressed in words. In contrast, spatio-temporal data sets containing control laws are usually available. In this study, a data-driven based 3D FLC design method using multiple single-output support vector regressions (SVRs) is proposed for SDDSs with multiple control sources. Firstly, in terms of the locally spatial influence feature of control sources on the space domain, a complex SDDS is decomposed into multiple SDDSs with one control source and a space-decomposition based 3D fuzzy control scheme is proposed. Secondly, multiple single-output SVRs with -insensitive cost function are used to learn and design multiple 3D FLCs from spatio-temporal data sets. Thirdly, a five-step design scheme is proposed, including space decomposition, data collection, spatial support-vector learning, 3D fuzzy rule construction, and 3D fuzzy controller integration. Finally, the proposed method is applied to a packed-bed reactor and simulation results were used to verify its effectiveness.

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Cited By

Li RWu H(2021)Iteration-based parameter identification and its applications about distributed parameter systemsApplied Soft Computing10.1016/j.asoc.2021.107300105:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107300
Zhang XLi HCheng CMa S(2019)Transfer learning based 3D fuzzy multivariable control for an RTP systemApplied Intelligence10.1007/s10489-019-01557-750:3(812-829)Online publication date: 9-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01557-7

Index Terms

Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Index terms have been assigned to the content through auto-classification.

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cover image Applied Soft Computing

Applied Soft Computing Volume 59, Issue C

October 2017

696 pages

ISSN:1568-4946

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2017

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Cited By

Li RWu H(2021)Iteration-based parameter identification and its applications about distributed parameter systemsApplied Soft Computing10.1016/j.asoc.2021.107300105:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107300
Zhang XLi HCheng CMa S(2019)Transfer learning based 3D fuzzy multivariable control for an RTP systemApplied Intelligence10.1007/s10489-019-01557-750:3(812-829)Online publication date: 9-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01557-7

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