A clustering and SVM regression learning-based spatiotemporal fuzzy logic controller with interpretable structure for spatially distributed systems

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. © 2012 xian-xia zhang et al.