摘要
逻辑优化是数字逻辑电路分析与设计的关键,对于降低系统复杂性,减少系统功耗和提高系统安全性有重要作用。随着数字逻辑电路规模的不断扩大,传统的理论将面临新的挑战。从知识工程角度看,逻辑优化的本质是知识约简的过程。粒计算(granular computing,Gr C)是处理大规模、复杂问题的人工智能新方法。在简述现有逻辑优化算法和粒计算理论发展现状的基础上,研究了粒计算理论中的等价关系、相容关系、覆盖等知识模型以及用粒矩阵刻画的知识发现算法,指出了将其应用于大规模数字逻辑电路逻辑优化的研究方向与研究思路。
Logic optimization is the key for the analysis and design of digital logic circuits, which will decrease sys- tem complexity and system cost, and improve system security. With the dramatically increasing complexity of the circuits, the traditional logic optimization theory and methods are facing new challenges. From view of knowledge engineering, the nature of the logic optimization is the knowledge reduction. Granular computing （GrC） is an effective way to deal with large-scale and complicated problems. Based on the review of current logic optimization algorithm and the development of GrC theory, this paper studies the knowledge model of granular computing theory, such as equivalence relation, tolerance relation, covering et al, and the knowledge discovery algorithm described by grznular matrices, and points out the idea of using the knowledge model and knowledge discovery algorithm to the logical optimization of large scale digital logic circuit.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第2期165-171,共7页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金
山西省回国留学人员科研资助项目
2011山西省留学回国人员科技活动择优资助项目~~
关键词
粒计算
数字逻辑电路
逻辑优化
granular computing （GrC）
digital logic circuit
logic optimization
