Novel algorithms for the image enhancement, filtering and edge detection using the fuzzy logic approach are proposed. An enhancement technique based on various combinations of fuzzy logic linguistic statements in the form of IF ...., THEN .... rules, modifies the image contrast and dynamic range of gray level, and provides a linguistic approach to image enhancement. Fuzzy filtering technique based on the tuning of fuzzy membership functions in the frequency donaia results in an improved restoration of an image which is degraded by additive random noise. An improved performance compared with traditional mask convolution filtering is also evident from the SNR (signal-tenoise ratio) improvement of 4.03 dB. The fuzzy edge detection algorithm provides a variety of edge information. A comparison of fuzzy edge detection algorithm with some existing edge detection algorithms by human observers is also shown to reveal the novelty.

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A complete design framework for a fuzzy constraint-based controller based on fuzzy constraint processing and its semantics and relationship to fuzzy logic is presented. Although, to date, many fuzzy logic control systems have been implemented in rule-based languages, we expect that eventually these languages will be supplanted by constraint-based languages. Despite the successes which have owed from the applications of rule-based fuzzy logic control systems, this paradigm o ers only a small part of the expressive competence of the rst-order predicate calculus. In addition, because constraints represent the requirements that the artifact being designed must satisfy, the design can be viewed as exploring alternatives in a solution space bounded by these constraints. In consequence, constraints are suitable to the task of modeling the controller in a dynamic control system so that the output is governed to a desired state as speci ed by the constraints. In this paper, the concept of \fuzzy constraints" in a problem solving is introduced and some basic de nitions of fuzzy constraint processing in a constraint network and its semantic modeling are addressed. Then a fuzzy local propagation inference mechanism for reasoning about imprecise information applying the lter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy logic controller (FLC) to a new type of controller, the fuzzy constraint-based controller (FCC), using a more general predicate calculus and full rst-order logic knowledge representation and making use of the idea of fuzzy constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and also demonstrates superior outcomes to a con-

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