模糊膜计算模型与应用研究综述
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摘要
膜计算是由生物细胞(群)相关机理启发的一类分布式、并行计算模型.膜计算模型已被证明是强大的并且以多项式时间复杂性求解众多的NP(non-deterministic polynomial)问题.膜计算模型与算法是膜计算领域的核心关键问题,特别是面向应用问题的模型和算法.模糊膜计算是近年开发的一种膜计算模型,它能克服先前模型在处理不确定性问题上的限制,得到极为广泛的关注.目前,模糊膜计算模型已在诸如电力系统故障诊断、微网控制中得到应用.首先简要地介绍几种模糊P系统,然后描述它们在工程问题中的应用.
Membrane computing is a class of distributed parallel computing models inspired from the mechanism of biological cells.Membrane computing model has been proven to be powerful and capable of solving a lot of NP(non-deterministic polynomial)-hard problems in a feasible polynomial time.Membrane computing model and algorithm are the key issues,especially problem-driven models and algorithms.Fuzzy membrane computing is a kind of membrane computing model developed recently,which can overcome the limitation on previous models on processing the uncertainty and has received a great deal of attention.Fuzzy membrane computing model has been successfully applied in fault diagnosis of power systems and micro-grid control.In this paper,we briefly introduced several fuzzy P systems,and then described their application in some engineering problems.
Membrane computing is a class of distributed parallel computing models inspired from the mechanism of biological cells.Membrane computing model has been proven to be powerful and capable of solving a lot of NP(non-deterministic polynomial)-hard problems in a feasible polynomial time.Membrane computing model and algorithm are the key issues,especially problem-driven models and algorithms.Fuzzy membrane computing is a kind of membrane computing model developed recently,which can overcome the limitation on previous models on processing the uncertainty and has received a great deal of attention.Fuzzy membrane computing model has been successfully applied in fault diagnosis of power systems and micro-grid control.In this paper,we briefly introduced several fuzzy P systems,and then described their application in some engineering problems.
引文
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[19]PENG H,WANG J,SHI P,et al.Fault diagnosis of power systems using fuzzy tissue-like P systems[J].Integrated Computer-Aided Engineering,2017,24:401-411.
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[22]HUANG S J,LIU X Z,SU W F,et al.Application of honey-bee mating optimization algorithm to fault section estimation in power systems[J].IEEE Transactions on Power Delivery,2013,28(3):1944-1951.
[23]XIONG G,SHI D,ZHU L,et al.A new approach to fault diagnosis of power systems using fuzzy reasoning spiking neural P systems[J].Mathematical Problems in Engineering,2013,2013(1):211-244.
[24]TU M,WANG J,PENG H,et al.Application of adaptive fuzzy spiking neural P systems in fault diagnosis model of power systems[J].Chinese Journal of Electronics,2014,23(1):87-92.
[25]WANG J,PENG H,TU M,et al.A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms[J].Chinese Journal of Electronics,2016,25(2):320-327.
[26]WANG T,ZHANG G,PREZ-JIMNEZ M J.Fault diagnosis models for electric locomotive systems based on fuzzy reasoning spiking neural P systems[C]//Lecture Notes in Computer Science,2014:385-395.
[27]WANG T,ZHANG G,PREZ-JIMNEZ M J,et al.Weighted fuzzy reasoning spiking neural P systems:application to fault diagnosis in traction power supply systems of high-speed railways[J].Journal of Computational and Theoretical Nanoscience,2015,12(7):1103-1114.
[28]IDOWU R K,CHANDREN R,OTHMAN Z A.Advocating the use of fuzzy reasoning spiking neural Psystem in intrusion detection[C]//Asian Conference on Membrane Computing(ACMC),2014:1-5.
[29]RUFAI K I.Attack detection in a rule-based system using fuzzy spiking neural P system[J].International Journal of Informatics and Communication Technology,2016,5(1):11-20.
[30]IDOWU RK,RAVIE CM,OTHMAN Z A.Denial of service attack detection using trapezoidal fuzzy reasoning spiking neural P system[J].Journal of Theoretical&Applied Information Technology,2015,75(3):397-404.
[31]HUANG K,WANG T,HE Y,et al.Temporal fuzzy reasoning spiking neural P systems with real numbers for power system fault diagnosis[J].Journal of Computational and Theoretical Nanoscience,2016,13(6):3804-3814.
[32]HUANG K,ZHANG G,WEI X,et al.Fault classification of power transmission lines using fuzzy reasoning spiking neural P systems[C]//Bio-inspired Computing-Theories and Applications,2017:109-117.
[33]XIONG G,SHI D,CHEN J.Implementing fuzzy reasoning spiking neural P system for fault diagnosis of power systems[C]//Power&Energy Society General Meeting,2013:1-8.
[34]WANG T,ZHANG G,RONG H,et al.Application of fuzzy reasoning spiking neural P systems to fault diagnosis[J].International Journal of Computers Communications&Control,2014,9(6):786-799.
[35]WANG J,CHEN K,LI M,et al.Cell-like fuzzy P system and its application in energy management of microgrid[J].Journal of Computational and Theoretical Nanoscience,2016,13(6):3643-3651.
[2]PUN G.Membrane computing:an introduction[M].New York:Springer,2002.
[3]PUN G,ROZENBERG G,SALOMAA A.The oxford handbook of membrane computing[M].New York:Oxford University Press,2010.
[4]张葛祥,程吉祥,王涛,等.膜计算:理论与应用[M].北京:科学出版社,2015.
[5]LEPORATI A,MAURI G,ZANDRON C,et al.Uniform solutions to SAT and subset sum by spiking neural P systems[J].Natural Computing,2009,8(4):681-702.
[6]ISHDORJ T O,LEPORATI A,PAN L,et al.Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources[J].Theoretical Computer Science,2010,411:2345-2358.
[7]SONG T,PAN L.Spiking neural P systems with rules on synapses working in maximum spiking strategy[J].IEEE Transaction on NanoBioscience,2015,14(4):465-477.
[8]ZHANG X,PAN L,PUN A.On the universality of axon P systems[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(11):2816-2829.
[9]IONESCU M,PUN G,YOKOMORI T.Spiking neural P systems[J].Fundameta Informaticae,2006,71(2/3):279-308.
[10]PENG H,WANG J,PREZ-JIMNEZ M J,et al.Fuzzy reasoning spiking neural P system for fault diagnosis[J].Information Sciences,2013,235:106-116.
[11]WANG J,SHI P,PENG H,et al.Weighted fuzzy spiking neural P systems[J].IEEE Transactions on Fuzzy Systems,2013,21(2):209-220.
[12]WANG J,PENG H.Adaptive fuzzy spiking neural P systems for fuzzy inference and learning[J].International Journal of Computer Mathematics,2013,90(4):857-868.
[13]WANG T,ZHANG G,ZHAO J,et al.Diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems[J].IEEE Transactions on Power Systems,2015,30(3):1182-1194.
[14]ATANASSOV K.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.
[15]PENG H,WANG J,MING J,et al.Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems[J].IEEE Transaction on Smart Grid,2017(1):267-272.
[16]PREZ-JIMNEZ M J,GRACIANI C.Fuzzy reasoning spiking neural P systems revisited:a formalization[J].Theoretical Computer Science,2017(1):4-14.
[17]MACAS-RAMOS L F,MARTNEZ-DEL-AMOR M A,PREZ-JIMNEZ M J.Simulating FRSN Psystems with real numbers in P-Lingua on sequential and CUDA platforms[C]//Lecture Notes in Computer Science,2015:672-679.
[18]WANG T,ZHANG G,PREZ-JIMNEZ M J.Fuzzy membrane computing theory and applications[J].International Journal of Computers,Communications&Control,2015,10(6):904-935.
[19]PENG H,WANG J,SHI P,et al.Fault diagnosis of power systems using fuzzy tissue-like P systems[J].Integrated Computer-Aided Engineering,2017,24:401-411.
[20]CHO H J,PARK J K.An expert system for fault section diagnosis of power systems using fuzzy relations[J].IEEE Transactions on Power Systems,1997,12(1):342-348.
[21]CARDOSO G,ROLIM J G,ZRN H H.Application of neural network modules to electric power system fault section estimation[J].IEEE Transactions on Power Delivery,2004,19(3):1034-1041.
[22]HUANG S J,LIU X Z,SU W F,et al.Application of honey-bee mating optimization algorithm to fault section estimation in power systems[J].IEEE Transactions on Power Delivery,2013,28(3):1944-1951.
[23]XIONG G,SHI D,ZHU L,et al.A new approach to fault diagnosis of power systems using fuzzy reasoning spiking neural P systems[J].Mathematical Problems in Engineering,2013,2013(1):211-244.
[24]TU M,WANG J,PENG H,et al.Application of adaptive fuzzy spiking neural P systems in fault diagnosis model of power systems[J].Chinese Journal of Electronics,2014,23(1):87-92.
[25]WANG J,PENG H,TU M,et al.A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms[J].Chinese Journal of Electronics,2016,25(2):320-327.
[26]WANG T,ZHANG G,PREZ-JIMNEZ M J.Fault diagnosis models for electric locomotive systems based on fuzzy reasoning spiking neural P systems[C]//Lecture Notes in Computer Science,2014:385-395.
[27]WANG T,ZHANG G,PREZ-JIMNEZ M J,et al.Weighted fuzzy reasoning spiking neural P systems:application to fault diagnosis in traction power supply systems of high-speed railways[J].Journal of Computational and Theoretical Nanoscience,2015,12(7):1103-1114.
[28]IDOWU R K,CHANDREN R,OTHMAN Z A.Advocating the use of fuzzy reasoning spiking neural Psystem in intrusion detection[C]//Asian Conference on Membrane Computing(ACMC),2014:1-5.
[29]RUFAI K I.Attack detection in a rule-based system using fuzzy spiking neural P system[J].International Journal of Informatics and Communication Technology,2016,5(1):11-20.
[30]IDOWU RK,RAVIE CM,OTHMAN Z A.Denial of service attack detection using trapezoidal fuzzy reasoning spiking neural P system[J].Journal of Theoretical&Applied Information Technology,2015,75(3):397-404.
[31]HUANG K,WANG T,HE Y,et al.Temporal fuzzy reasoning spiking neural P systems with real numbers for power system fault diagnosis[J].Journal of Computational and Theoretical Nanoscience,2016,13(6):3804-3814.
[32]HUANG K,ZHANG G,WEI X,et al.Fault classification of power transmission lines using fuzzy reasoning spiking neural P systems[C]//Bio-inspired Computing-Theories and Applications,2017:109-117.
[33]XIONG G,SHI D,CHEN J.Implementing fuzzy reasoning spiking neural P system for fault diagnosis of power systems[C]//Power&Energy Society General Meeting,2013:1-8.
[34]WANG T,ZHANG G,RONG H,et al.Application of fuzzy reasoning spiking neural P systems to fault diagnosis[J].International Journal of Computers Communications&Control,2014,9(6):786-799.
[35]WANG J,CHEN K,LI M,et al.Cell-like fuzzy P system and its application in energy management of microgrid[J].Journal of Computational and Theoretical Nanoscience,2016,13(6):3643-3651.