Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks

We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome "corrective" terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.