On a fuzzy-neural hierarchical controller with a self-generating knowledge base

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. We modify Berenji and Khedkar´s (1992) GARIC architecture to a hierarchical controller and enable it to automatically generate a knowledge base. A pseudo-supervised learning scheme using reinforcement learning and error backpropagation is employed. Example applications are provided to underscore its viability