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基于整数非线性规划的总线胚胎电子系统细胞数目优选 被引量:2

Optimal Selection of Cell Number of Bus-based Embryonic Electronic System Based on Integer Nonlinear Programming Model
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摘要 在规模一定的总线胚胎电子系统中,电子细胞数目将直接影响电子系统的硬件消耗与可靠性。针对电子细胞数目选择缺乏定量分析方法的现状,提出了一种基于整数非线性规划的电子系统内电子细胞数目优选方法。基于n/k系统可靠性理论,建立了电子系统的可靠性分析模型。以系统消耗的MOS场效应管数目为衡量指标,建立了电子系统的硬件消耗分析模型。通过分析系统可靠性和硬件消耗,将电子系统内电子细胞数目优选问题转化为求解整数非线性规划问题,并基于遗传算法实现了胚胎电子系统细胞数目的优选。仿真实验及分析结果表明,该方法能够较好地解决系统中电子细胞数目优选问题。 In bus-based embryonic electronic system (BEES) with certain scale, the number of electronic cells has a direct effect on the reliability and hardware consumption of BEES. In view of the research status that the selection of electronic cell number is lack of quantitative analysis method, an optimal selection method of electronic cell number for BEES is proposed, which is based on integer nonlinear programming model. A BEES reliability analysis model is established based on n/k system reliability theory. The number of metal oxide semiconductor (MOS) field effect transistors which are consumed in the system is used to measure hardware consumption, and BEES hardware consumption analysis model is established. The selection problem of BEES electronic cell number is transformed into the solving problem of integer nonlinear programming by analyzing BEES reliability and hardware consumption, and the optimal selection of BEES electronic cell number is realized based on genetic algorithm. The simulation experiment and analysis results indicate that the proposed method can be used for the optimal selection of electronic cell number in BEES.
作者 王涛 蔡金燕 张民国 孟亚峰 朱赛 WANG Tao;CAI Jin-yan;ZHANG Min-guo;MENG Ya-feng;ZHU Sai(Department of Electronic and Optical Engineering,Shijiazhuang Campus of Army Engineering University,Shijiazhuang 050003,Hebei,China;Unit 73907 of PLA,Fuzhou 350003,Fujian,China)
出处 《兵工学报》 EI CAS CSCD 北大核心 2018年第6期1132-1143,共12页 Acta Armamentarii
基金 国家自然科学基金项目(61601495)
关键词 电子细胞 胚胎仿生 总线 整数非线性规划 自修复 可靠性 硬件消耗 遗传算法 electronic cell embryonic bio-inspiring bus integer nonlinear programming self-repairing reliability hardware consumption genetic algorithm
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