## 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 language | English (US) |
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Pages (from-to) | 2766-2767 |

Number of pages | 2 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

Volume | 5 |

DOIs | |

State | Published - 1990 |

Externally published | Yes |

Event | Proceedings of the 29th IEEE Conference on Decision and Control Part 5 (of 6) - Honolulu, HI, USA Duration: Dec 5 1990 → Dec 7 1990 |

## All Science Journal Classification (ASJC) codes

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization