A Neural Approach of Fuzzy Operators

Abstract

Real world applications of fuzzy sets call for a variety of systems realizing fuzzy computation. A special focus is to develop some universal computing models, easy customizing to meet wide subjects of particular specifications. For this purpose, it is indispensable to identify a few generic-processing modules, which may be configured to perform general computations on fuzzy sets. A family of logic-based neurons emerges as a collection of processing operations whose role is to model logic oriented processing of fuzzy sets. With a generalized fuzzy neuron it is desirable to add yet another level of programmability, parametric learning. This fuzzy neuron utilizes in-situ learning, via fuzzy backpropagation, to adjust the interconnect strength between neurons. This combination of generalized fuzzy computation and adaptivity, creates a powerful processing element.