A Clustering and SVM Regression Learning‐Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

Xian-xia Zhang, Jun-da Qi, Bai-li Su, Shi-wei Ma and Hong-bo Liu

Journal of Applied Mathematics, 2012, vol. 2012, issue 1

Abstract: Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3‐D FLC was developed for such systems. The previous study on the 3‐D FLC was concentrated on an expert knowledge‐based approach. However, in most of situations, we may lack the expert knowledge, while input‐output data sets hidden with effective control laws are usually available. Under such circumstance, a data‐driven approach could be a very effective way to design the 3‐D FLC. In this study, we aim at developing a new 3‐D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule‐base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule‐base is simplified by merging similar 3‐D fuzzy sets and similar 3‐D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3‐D fuzzy system is discussed. Finally, the control of a catalytic packed‐bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3‐D FLC design.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2012/841609

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2012:y:2012:i:1:n:841609

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().