Implementation of minimum error expert system

The author shows how to implement a neural net expert system that is optimum in the minimum error sense and recognizes objects based on feature extraction. The expert system can handle features that may not be appropriate to describe certain objects (termed don´t care features) or features that cannot be extracted (unknowns or don´t know features) by using marginal density functions, if needed. The implementation uses linear operations and trivial nonlinear transfer functions and is amenable to VLSI implementation. The application to a real-world problem of identifying ordnance is discussed