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 |
巻 | E92-A |
号 | 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