Neural network logic system

Abstract

Abstract A novel neural network implementation for logic systems has been developed. The neural network can determine whether a particular logic system and knowledge base are self-consistent, which can be a difficult problem for more complex systems. Through neural network hardware using parallel computation, valid solutions may be found more rapidly than could be done with previous, software-based implementations. This neural network is particularly suited for use in large, real-time problems, such as in a real-time expert system for testing the consistency of a programmable process controller, for testing the consistency of an integrated circuit design, or for testing the consistency of an "expert system." This neural network may also be used as an "inference engine," i.e., to test the validity of a particular logical expression in the context of a given logic system and knowledge base, or to search for all valid solutions, or to search for valid solutions consistent with given truth values which have been "clamped" as true or false. The neural network may be used with many different types of logic systems: those based on conventional "truth table" logic, those based on a truth maintenance system, or many other types of logic systems. The "justifications" corresponding to a particular logic system and knowledge base may be permanently hard-wired by the manufacturer, or may be supplied by the user, either reversibly or irreversibly.

Inventors

• 👤 Suresh Guddanti
• 👤 William P. Mounfield, Jr.

Bibliographic Details