Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function | Zendy

Pong P. Chu | Zendy

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Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function

Author(s) -

Pong P. Chu

Publication year - 1993

Publication title -

vlsi design

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.123

H-Index - 24

eISSN - 1065-514X

pISSN - 1026-7123

DOI - 10.1155/1995/64953

Subject(s) - implicant , maxima and minima , artificial neural network , boolean function , hopfield network , boolean network , cover (algebra) , computer science , simple (philosophy) , function (biology) , set (abstract data type) , process (computing) , algorithm , mathematical optimization , mathematics , boolean expression , artificial intelligence , engineering , mechanical engineering , mathematical analysis , philosophy , epistemology , evolutionary biology , biology , programming language , operating system

To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set ofimplicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical forlarge input data size. Neural network approach is used to solve this problem. We first formalize the problem, andthen define an “energy function” and map it to a modified Hopfield network, which will automatically search forthe minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most ofthe time

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