An improved local search learning method for multiple-valued logic network minimization with Bi-objectives

Shangce Gao*, Qiping Cao, Catherine Vairappan, Jianchen Zhang, Zheng Tang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This paper describes an improved local search method for synthesizing arbitrary Multiple-Valued Logic (MVL) function. In our approach, the MVL function is mapped from its algebraic presentation (sum-of-products form) on a multiple-layered network based on the functional completeness property. The output of the network is evaluated based on two metrics of correctness and optimality. A local search embedded with chaotic dynamics is utilized to train the network in order to minimize the MVL functions. With the characteristics of pseudo-randomness, ergodicity and irregularity, both the search sequence and solution neighbourhood generated by chaotic variables enables the system to avoid local minimum settling and improves the solution quality. Simulation results based on 2-variable 4-valued MVL functions and some other large instances also show that the improved local search learning algorithm outperforms the traditional methods in terms of the correctness and the average number of product terms required to realize a given MVL function.

Original languageEnglish
Pages (from-to)594-603
Number of pages10
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE92-A
Issue number2
DOIs
StatePublished - 2009/02

Keywords

  • Bi-objectives
  • Chaos
  • Local search
  • Minimization
  • Multiple-valued logic
  • Network

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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