Fuzzy constraint networks for process control

One of the criticisms of rule-based fuzzy controllers has been their use of only shallow knowledge bases. This shallowness is primarily attributable to the expressiveness of rule-based systems, which is that of the Horn clause subset of first-order predicate calculus (FOPC). We would argue that the language in which declarative knowledge is represented must be at least as expressive as the full FOPC. We propose using a constraint-based system which makes available the full FOPC and hence is richer in expressiveness than rule-based systems. We introduce the notion of fuzzy constraint networks and compare them to fuzzy rule-based systems. The inference mechanism for such a fuzzy constraint-based system must ensure that the inferred knowledge is consistent with the constraints of the problem domain. We introduce one such inference mechanism, fuzzy T-local propagation, and prove that this algorithm preserves consistency of a fuzzy constraint network. We illustrate the advantages of a fuzzy constraint-based control by comparing its inference with that of a fuzzy rule-based controller