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  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.2   pp.594-603
Publication Date: 2009/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.594
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
Keyword: 
Multiple-Valued Logic,  local search,  minimization,  bi-objectives,  chaos,  network,  

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Summary: 
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.