基于二进神经网络的0/1分布系统可靠性分析

杨娟; 陆阳; 黄镇谨

doi:10.3724/SP.J.1004.2014.01472

基于二进神经网络的0/1分布系统可靠性分析

doi: 10.3724/SP.J.1004.2014.01472

杨娟^1,2,
陆阳¹,
黄镇谨^1,3

1.
合肥工业大学计算机与信息学院合肥 230009;
2.
合肥工业大学信息与通信工程博士后科研流动站合肥 230009;
3.
广西工学院计算机工程系柳州 545006

基金项目:

安徽省自然科学基金项目（1408085QF117），合肥工业大学博士专项科研资助基金（2013HGBZ0182），合肥工业大学青年教师创新项目（2013HGQC0019）资助

详细信息

作者简介:
杨娟合肥工业大学计算机与信息学院，讲师. 2012 年获合肥工业大学计算机与信息学院博士学位. 主要研究方向为人工智能，神经网络.E-mail：yangjuan6985@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2013-03-12
- 修回日期: 2013-12-09
- 刊出日期: 2014-07-20

Reliability of Systems with 0/1 Distribution Based on Binary Neural Networks

1.
School of Computer and Information, Hefei University of Technology, Hefei 230009;
2.
Postdoctoral Programs at Information and Communication Engineering, Hefei University of Technology, Hefei 230009;
3.
Department of Computer Engineering, Guangxi University of Technology, Liuzhou 545006

Funds:

Supported by Natural Science Foundation of Anhui Province (1408085QF117), Doctoral Special Research Fund of Hefei University of Technology (2013HGBZ0182), Young Teacher Innovation Project of Hefei University of Technology (2013HGQC0019)

摘要

摘要: 系统可靠性的计算依赖于各基本单元的0/1分布关系及其构成的布尔逻辑. 本文利用二进神经网络可以完备实现布尔逻辑的特性，提出一种基于二进神经网络的可靠性分析方法. 该方法针对每个二进神经元的输入都是0/1逻辑关系的线性组合这一特点，提出并且证明了0/1分布的线性组合的概率分布函数；建立系统功能与布尔函数间的等价关系，将系统转化为相应的二进神经网络；利用线性组合的概率分布函数，通过逐层计算该二进神经网络的0/1输出概率，解决了一般系统的可靠性计算问题.
- 二进神经网络 /
- 系统可靠性 /
- 分布函数 /
- 线性组合
Abstract: The computing of system reliability relies on the relationship of 0/1 distribution of components and their boolean logic. With the help of the characteristic that binary neural networks can complete the whole boolean function, we propose a method of reliability analysis based on binary neural networks. According to the input of every binary neuron is a 0 or 1 logic variable, we provide and prove the distribution function of the linear combination of 0/1 distribution. Then the equivalent relation between the system function and boolean function is established, and the system is converted to an equivalent binary neural network. As a result, using the distribution function, we can successfully resolve the problem of reliability analysis of general systems by computing the 0/1 output probability layer by layer.
- Binary neural networks /
- system reliability /
- distribution function /
- linear combination

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参考文献(15)

[1]	Laprie J C. Dependability: Basic Concepts and Terminology. Vienna: Springer-Verlag, 1990
[2]	Mitchell C, Stavridou V. Mathematics of Dependable Systems. Oxford: Clarendon Press, 1995
[3]	Cao Jin-Hua, Cheng Kan. Reliability Mathematical Introduction. Beijing: Higher Education Press, 2006(曹晋华, 程侃. 可靠性数学引论. 北京: 高等教育出版社, 2006)
[4]	Chaudhuri G, Hu K, Afshar N. A new approach to system reliability. IEEE Transactions on Reliability, 2001, 50(1): 75-84
[5]	Lin M S. An O(k2log(n)) algorithm for computing the reliability of consecutive-k-out-of-n: F systems. IEEE Transactions on Reliability, 2004, 53(1): 3-6
[6]	Amari S V, Zuo M J, Dill G. A fast and robust reliability evaluation algorithm for generalized multi-state k-out-of-n system. IEEE Transactions on Reliability, 2009, 58(1): 88-97
[7]	Azaron A, Katagiri H, Sakawa M, Modarres M. Reliability function of a class of time-dependent systems with standby redundancy. European Journal of Operational Research, 2005, 164(2): 378-386
[8]	Yang Juan, Lu Yang, Huang Zhen-Jin, Wang Qiang. Hamming sphere dimple in binary neural networks and its linear separability. Acta Automatica Sinica, 2011, 37(6): 737-745 (杨娟, 陆阳, 黄振谨, 王强. 二进神经网络中的汉明球突及其线性可分性. 自动化学报, 2011, 37(6): 737-745)
[9]	Chen F Y, Chen G R, He G L, Xu X B, He Q B. Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations. IEEE Transactions on Neural Network, 2009, 20(10): 1645-1658
[10]	Chen F Y, Chen G R, He Q B. Universal perceptron and DNA-like learning algorithm for binary neural networks: non-LSBF implementation. IEEE Transactions on Neural Network, 2009, 20(8): 1293-1301
[11]	Lu Y, Yang J, Wang Q, Huang Z J. The upper bound of the minimal number of hidden neurons for the parity problem in binary neural networks. Science China Information Sciences, 2012, 55(7): 1579-1587
[12]	Yang Juan, Lu Yang, Fang Huan, Zhu Xiao-Juan. An ant colony-based learning algorithm for binary neural networks. Journal of Circuits and Systems, 2012, 17(6): 49-56 (杨娟, 陆阳, 方欢, 朱晓娟. 基于蚁群算法的二进神经网络学习算法. 电路与系统学报, 2012, 17(6): 49-56)
[13]	Lu Yang, Han Jiang-Hong, Wei Zhen. A general judging and constructing method of SP functions in binary neural networks. Acta Automatica Sinica, 2003, 29(2): 234-241(陆阳,韩江洪,魏臻.二进神经网络中SP函数的一般判别和构造方法.自动化学报, 2003, 29(2): 234-241)
[14]	Lu Yang, Han Jiang-Hong, Zhang Wei-Yong. Logical relation determination criteria and equivalence rule extraction on binary neural networks. Pattern Recognition and Artificial Intelligence, 2001, 14(2): 171-176 (陆阳, 韩江洪, 张维勇. 二进神经网络逻辑关系判据及等价性规则提取. 模式识别与人工智能, 2001, 14(2): 171-176)
[15]	Kim J H, Park S. The geometrical learning of binary neural networks. IEEE Transaction on Neural Networks, 1995, 6(1): 237-247