Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learning
| Autor: | Xian-Xia Zhang, Jia-jia Li, Bing Wang, Lian-rong Zhao, Gui-tao Cao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Fuzzy rule
Dynamical systems theory Computer science Distributed computing 010103 numerical & computational mathematics 02 engineering and technology Fuzzy control system computer.software_genre 01 natural sciences Fuzzy logic Expression (mathematics) Support vector machine Nonlinear system Control theory 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Data mining 0101 mathematics computer Software |
| Zdroj: | Applied Soft Computing. 59:378-388 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2017.04.064 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect