Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function
| Autor: | Pong P. Chu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 1995 |
| Předmět: |
Optimization
Artificial neural network Computer science Quine-McCluskey method Computer Graphics and Computer-Aided Design lcsh:QA75.5-76.95 Hopfield-network Hopfield network Maxima and minima Recurrent neural network Boolean network Hardware and Architecture Quine–McCluskey algorithm Logic minimization lcsh:Electronic computers. Computer science Electrical and Electronic Engineering Stochastic neural network Boolean function Algorithm Neural networks |
| Zdroj: | VLSI Design, Vol 3, Iss 1, Pp 13-19 (1995) |
| ISSN: | 1065-514X |
| DOI: | 10.1155/1995/64953 |
| Popis: | To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an “energy function” and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time. |
| Databáze: | OpenAIRE |
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