IEICE TRANSACTIONS on Fundamentals
An Improved Local Search Learning Method for Multiple-Valued Logic Network Minimization with Bi-objectives
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.2 pp.594-603
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.594
- Type of Manuscript
- PAPER
- Category
- Neural Networks and Bioengineering
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Keyword
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Shangce GAO, Qiping CAO, Catherine VAIRAPPAN, Jianchen ZHANG, Zheng TANG, "An Improved Local Search Learning Method for Multiple-Valued Logic Network Minimization with Bi-objectives" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 594-603, February 2009, doi: 10.1587/transfun.E92.A.594.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.594/_p
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@ARTICLE{e92-a_2_594,
author={Shangce GAO, Qiping CAO, Catherine VAIRAPPAN, Jianchen ZHANG, Zheng TANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Improved Local Search Learning Method for Multiple-Valued Logic Network Minimization with Bi-objectives},
year={2009},
volume={E92-A},
number={2},
pages={594-603},
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.},
keywords={},
doi={10.1587/transfun.E92.A.594},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - An Improved Local Search Learning Method for Multiple-Valued Logic Network Minimization with Bi-objectives
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 594
EP - 603
AU - Shangce GAO
AU - Qiping CAO
AU - Catherine VAIRAPPAN
AU - Jianchen ZHANG
AU - Zheng TANG
PY - 2009
DO - 10.1587/transfun.E92.A.594
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - 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.
ER -
English
