LOGIC-BASED NEURAL NETWORKS

Realization of two-valued or many-valued functions carried out by fuzzy set logical operations is studied. Two basic types of computational nodes performing generalized (weighted) AND and OR operations form generic components of neural networks. Their functional and numerical properties arc carefully studied. The combination of these two nodes into a structure of logic processors is used to approximate many-input-single-output logical relationships. Relevant learning algorithms are proposed and analyzed. Numerical considerations are included as well. Generalization aspects of this architecture are studied. A new interpretation of the paradigm of a fuzzy controller realized as a cerebellar model articulation controller structure with the aid of logic processors are discussed as well.