Share Email Print
cover

Proceedings Paper

Predicate calculus for an architecture of multiple neural networks
Author(s): Robert H. Consoli
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

Paper Details

Date Published: 1 August 1990
PDF: 10 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21193
Show Author Affiliations
Robert H. Consoli, GTE Government Systems Corp. (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

© SPIE. Terms of Use
Back to Top