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

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

7 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)594-603
ページ数10
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E92-A
2
DOI
出版ステータス出版済み - 2009/02

ASJC Scopus 主題領域

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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