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

Abstract EN

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.