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Functional decomposition of MVL functions using multi-valued decision diagrams

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3 Author(s)
Files, C. ; Dept. of Electr. Eng., Portland State Univ., OR, USA ; Drechsler, R. ; Perkowski, M.A.

In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning samples can be implemented as minterms in multi-valued logic. The representation, can then be decomposed into smaller blocks, resulting in a reduced problem complexity. This gives induced descriptions through structuring, or feature extraction, of a learning problem. Our approach to the decomposition is based on expressing a multi-valued function (learning problem) in terms of a multi-valued decision diagram that allows the use of Don't Cares. The inclusion of Don't Cares is the emphasis for this paper since multi-valued benchmarks are characterized as having many Don't Cares

Published in:

Multiple-Valued Logic, 1997. Proceedings., 1997 27th International Symposium on

Date of Conference:

28-30 May 1997