Design of two architectures of asynchronous binary neural networks using linear programming

M. Kam, J. C. Chow, R. Fischl

Research output: Contribution to journalConference articlepeer-review

Abstract

A novel design technique for asynchronous binary neural networks is proposed. This design uses linear programming to design two architectures: i) a fully connected network that reads a N-digit cue and classifies it into a category represented by a N-digit pattern; and ii) a two-layer network (with lateral connections) that has M neurons in the first layer and L neurons in the second layer; the network reads an M-digit cue to the first layer and associates it with a second-layer L-digit pattern. In both cases, the objective function is a weighted sum of the number of errors that can be corrected by the network. A cue with this number of errors (or fewer) is guaranteed to converge to the correct pattern. An economical VLSI realization of the designed networks can be easily accomplished.

Original languageEnglish (US)
Pages (from-to)2766-2767
Number of pages2
JournalProceedings of the IEEE Conference on Decision and Control
Volume5
DOIs
StatePublished - Jan 1 1990
Externally publishedYes
EventProceedings of the 29th IEEE Conference on Decision and Control Part 5 (of 6) - Honolulu, HI, USA
Duration: Dec 5 1990Dec 7 1990

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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