Abstract
This paper presents a rule-based approach to the problem known as channel equalization which is concerned with reconstructing binary signals being transmitted through a dispersive communication channel and then corrupted by additive noise. With the aid of fuzzy concepts and fuzzy operators, we try to cope with the problem by a rule-based equalizer. A self-organizing algorithm consisting of learning, pruning, and refining processes is developed aiming at building the rule-base from labeled observations. The rule-based equalizer makes the decision on the basis of measuring the similarity between the current observation and the obtained rule prototypes. The simulation studies on linear and nonlinear channels are used to demonstrate the performance of the proposed approacht.
Get full access to this article
View all access options for this article.