Training a hybrid neural-fuzzy system

Abstract

It is shown that hybrid neural-fuzzy systems can be described almost as concisely as conventional layered neural networks and can be subjected to the same methods for training. Combining elements of neural and fuzzy systems in this way offers clear benefits whenever the training a neural network can be improved by incorporation of prior knowledge or where a fuzzy system requires careful tuning. The examples suggest that the inclusion of fuzzy elements in a neural network framework may, for certain applications, increase representational power with fewer parameters than would be required by merely increasing the number of conventional nodes and layers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>