Fuzzy Membrane Computing: Theory and Applications
Keywords:
fuzzy membrane computing, fuzzy set, multi-fuzzy set, membrane computing, fuzzy reasoning spiking neural P systems, trapezoidal fuzzy number, linguistic term.Abstract
Fuzzy membrane computing is a newly developed and promising research direction in the area of membrane computing that aims at exploring the complex in- teraction between membrane computing and fuzzy theory. This paper provides a comprehensive survey of theoretical developments and various applications of fuzzy membrane computing, and sketches future research lines. The theoretical develop- ments are reviewed from the aspects of uncertainty processing in P systems, fuzzifica- tion of P systems and fuzzy knowledge representation and reasoning. The applications of fuzzy membrane computing are mainly focused on fuzzy knowledge representation and fault diagnosis. An overview of different types of fuzzy P systems, differences between spiking neural P systems and fuzzy reasoning spiking neural P systems and newly obtained results on these P systems are presented.
References
Păun, G. (2000); Computing with Membranes, J Comput. Syst. Sci., ISSN 0022-0000, 61(1): 108-143.
Păun, G.; Rozenberg, G.; Salomaa, A. (2010); The Oxford Handbook of Membrane Computing, Oxford University Press, New York, NY, USA.
Zhang, G.X.; Gheorghe, M.; Pan L.Q.; Pérez-Jiménez, M.J. (2014); Evolutionary Membrane Computing: A Comprehensive Survey and New Results, Inform. Sciences, ISSN 0020-0255, 279: 528-551. http://dx.doi.org/10.1016/j.ins.2014.04.007
Ionescu, M.; Păun, G.; Yokomori T. (2006); Spiking Neural P Systems, Fund. Inform., ISSN 0169-2968, 71(2-3): 279-308.
Păun, G.; Pérez-Jiménez, M.J.; Rozenberg, G. (2006); Spike Trainss in Spiking Neural P Systems, Int. J. Found. Comput. S., ISSN 0129-0541, 17(4): 975-1002.
Cavaliere, M.; Ibarra, O.H.; Păun, Gh.; Egecioglu, O.; Ionescu, M.; Woodworth, S. (2009); Asynchronous Spiking Neural P Systems, Theor. Comput. Sci., ISSN 0304-3975, 410(24-25): 2352-2364.
Pan, L.Q.; Zeng, X.X. (2011); Small Universal Spiking Neural P Systems Working in Exhaustive Mode, IEEE T. Nanobiosci., ISSN 1536-1241, 10(2): 99-105.
Zhang, X.Y.; Luo, B.; Fang, X.Y.; Pan, L.Q. (2012); Sequential spiking neural P systems with exhaustive use of rules, BioSystems, ISSN 0303-2647, 108(1-3): 52-62.
Francis G.C.; Henry N.A. (2012); On Structures and Behaviors of Spiking Neural P Systems and Petri Nets, Lect. Notes in Comput. Sc., ISSN 0302-9743, vol. 7762: 145-160.
Song, T.; Pan, L.Q.; Păun, G. (2013); Asynchronous Spiking Neural P Systems with Local Synchronization, Inform. Sciences, ISSN 0020-0255, 219: 197-207.
Zhang, G.X., Rong H.N., Neri, F., Pérez-Jiménez M.J. (2014); An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems, Int. J. Neural. Syst., ISSN: 0129-0657, 24(5): 1440006 (16 pages).
Peng, H.; Wang, J.; Pérez-Jiménez, M.J.; Wang, H.; Shao, J.; Wang, T. (2013); Fuzzy Reasoning Spiking Neural P System for Fault Diagnosis, Inform. Sciences, ISSN 0020-0255, 235(20): 106-116.
Xiong, G.J.; Shi, D.Y.; Chen, J.F. (2013); Implementing fuzzy reasoning spiking neural P system for fault diagnosis of power systems, IEEE Power Energy Soc. Gen. Meet., Article ID 5970635, 5 pages.
Xiong, G.J.; Shi, D.Y.; Zhu, L.; Duan, X.Z. (2013); A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems, Math. Probl. Eng., ISSN 1024-123X, vol. 2013: Article ID 815352, 13 pages.
Wang T.; Zhang G.X.; Pérez-Jiménez M.J. (2014); Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems, Lect. Notes Comput. Sci. (CMC 2014), vol. 8961, pp. 361-374.
Wang, J.; Peng, H. (2010); Fuzzy Knowledge Representation Based on An Improving Spiking Neural P Systems, Proc. ICNC, pp. 3012-3015. http://dx.doi.org/10.1109/icnc.2010.5584281
Wang, J.; Peng, H. (2011); An Extended Spiking Neural P Systems for Fuzzy Knowledge Representation, Int. J. Innov. Comput. Inf. Control, ISSN 1349-4198, 7(7): 3709-3724.
Wang, J.; Shi, P.; Peng, H.; Pérez-Jiménez M.J.; Wang, T. (2013); Weighted Fuzzy Spiking Neural P Systems, IEEE T. Fuzzy Syst., ISSN 1063-6706, 21(2): 209-220.
Wang T.; Zhang G.X.; Pérez-Jiménez M.J.; Cheng J.X. (2015); Weighted Fuzzy Reasoning Spiking Neural P Systems: Application to Fault Diagnosis in Traction Power Supply Systems of High-Speed Railways, J. Comput. Theor. Nanos., ISSN 1546-1955, 12(7): 1103-1114. http://dx.doi.org/10.1166/jctn.2015.3857
Wang, J.; Peng, H. (2013); Adaptive Fuzzy Spiking Neural P Systems for Fuzzy Inference and Learning, Int. J. Comput. Math., ISSN 0020-7160, 90(4): 857-868.
Tu M.; Wang, J.; Peng, H.; Shi Peng (2014); Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems, Chinese J. Electron., ISSN 1022-4653, 23(1): 87-92.
Wang, T.; Wang, J.; Peng, H.; Deng Y.L. (2010); Knowledge Representation Using Fuzzy Spiking Neural P systems, Proc. IEEE BIC-TA, pp. 586-590.
Wang, T.; Wang, J.; Peng, H.; Wang, H. (2011); Knowledge Representation and Reasoning Based on FRSN P System, Proc WCICA, pp. 849-854.
Wang T.; Zhang G.X.; Rong H.N.; Pérez-Jiménez M.J. (2014); Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis, Int. J. Comput. Commun., ISSN 1841-9836, 9(6): 786-799.
Wang, T.; Zhang, G.X.; Zhao, J.B; He, Z.Y; Wang, J.; Pérez-Jiménez M.J. (2015); Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems, IEEE T. Power Syst., ISSN 0885-8950, 30(3): 1182-1194.
Zadeh, L.A. (1965); Fuzzy Sets, Inform. Contr., ISSN: 0019-9958, 8(3): 328-353.
Guiffrida, A.L.; Nagi, R. (1998); Fuzzy Set Theory Applications in Production Management Research: A Literature Survey, J. Intell. Manuf., ISSN: 0956-5515, 9(1): 39-56. http://dx.doi.org/10.1023/A:1008847308326
Zadeh, L.A. (1994); Fuzzy Logic Technology and Their Application, IEEE Publications, 1994.
Chen, S.M. (1996); A Fuzzy Reasoning Aproach for Rule-based Systems Based on Fuzzy Logics, IEEE T. Syst. Man Cy. B, ISSN 1083-4419, 26(5): 769-778.
Liu, H.C.; Liu, L.; Lin, Q.L.; Liu, N. (2013); Knowledge Acquisition and Representation Using Fuzzy Evidential Reasoning and Dynamic Adaptive Fuzzy Petri Nets, IEEE T. Cybern., ISSN 1083-4419, 43(3): 1059-1072.
Chen, W.H. (2011); Fault Section Estimation Using Fuzzy Matrix-based Reasoning Methods, IEEE T. Power Deliver., ISSN 0885-8977, 26(1): 205-213.
Sun, J.; Qin, S.Y.; Song, Y.H. (2004); Fault Diagnosis of Electric Power Systems Based on Fuzzy Petri Nets, IEEE T. Power Syst., ISSN 0885-8950, 19(4): 2053-2059.
Choi, C.; Kim, C.; Sung, N.; Park, Y. (2007); Evaluating the Quality of Service in Mobile Business Based on Fuzzy Set Theory, Proc. FSKD, pp. 483-487.
Chaira, T. (2012); Medical Image Enhancement Using Intuitionistic Fuzzy Set, Proc. RAIT, pp. 54-57. http://dx.doi.org/10.1109/rait.2012.6194479
Păun, Gh. (2000); Computing with Membranes (P Systems): Twenty Six Research Topics, available at http://psystems.disco.unimib.it/download/probl.pdf.
Nola, A.D.; Păun, G.; Pérez-Jiménez, M.J.; Rosselló, F. (2004); (Imprecise Topics about) Handling Imprecision in P Systems, Proc. BWUMC, pp. 1-10.
Păun, G. (2005); Further Twenty Six Open Problems in Membrane Computing, Proc. BWMC, pp. 249-262.
Păun, G. (2007); Tracing Some Open Problems in Membrane Computing, Proc. BWMC, ISSNl 1453-8245, 10(4): 303-314.
Obtułowicz A.; Păun, Gh. (2003); (In Search of) Probabilistic P Systems, Biosystems, ISSNl 0303-2647, 70(2): 107-121.
Obtułowicz A. (2003); Mathematical Models of Uncertainty with A Regard to Membrane Systems, Nat. Comput., ISSN 1567-7818, 2: 251-263.
Obtułowicz A. (2005); General Multi-fuzzy Sets and Fuzzy Membrane Systems, Lect. Notes Comput. Sci., ISSN 0302-9743, 3365: 359-372.
Casasnovas, J.; Miro, J.; Moyá, M.; Rosselló, F. (2004); A Fuzzy Approach to Membrane Computing with Approximate Copies, Proc. BWUMC: 121-127.
Casasnovas, J.; Miro, J.; Moyá, M.; Rosselló, F. (2004); An Approach to Membrane Computing Under Inexactitude, Int. J. Found. Comput. S., ISSN 0129-0541, 15(6): 841-864.
Casasnovas, J.; Rosselló, F. (2005); Fuzzy P Systems and Their Applications in Computational Biology, Proc. EUSFLAT, pp. 1112-1117.
Aguzzoli, S.; Besozzi, D., Gerla, B., Manara, C. (2004); P Systems with Vague Boundaries: the T-norm Approach, Proc. BWUMC, pp. 97-105.
Aguzzoli, S.; Ardelean, I.I.; Besozzi, D.; Gerla, B., Manara, C (2004); P Systems Under Uncertainty: the Case of Transmembrane Proteins, Proc. BWUMC, pp. 107-117.
Syropoulos A. (2006); Fuzzifying P Systems, Comput. J., ISSN 0010-4620, 49(5): 619-628.
Syropoulos, A. (2012); On Generalized Fuzzy Multisets and Their Use in Computation, Iran. J. Fuzzy Syst., ISSN 1735-0654, 9(2): 113-125.
Wu, S.; He, Z.Y.; Qian, C.H.; Zang, T.L. (2011); Application of Fuzzy Petri Net in Fault Diagnosis of Tranction Power Supply System for High-speed Way, Power System Tech., ISSN 1000-3673, 35(9): 79-85.
Yang, J.W.; He, Z.Y.; Zang, T.L. (2010); Power System Fault-diagnosis Method Based on Directional Weighted Fuzzy Petri Nets, Proc. CSEE., ISSN 0258-8013, 30(34): 42-49.
Chang, C.S.; Chen, J.M.;Srinivasan, D., Wen, F.S.; Liew, A.C. (1997); Fuzzy Logic Approach in Power System Fault Section Identification, IEE P. Gener. Transm. D., 144(5): 406-414. http://dx.doi.org/10.1049/ip-gtd:19971278
Lin, X.N.; Ke, S.H.; Li, Z.T.; Weng, H.L.; Han, X.H. (2010); A Fault Diagnosis Method of Power Systems Based on Improved Objective Function and Genetic Algorithm-tabu Search, IEEE T. Power Deliver., ISSN 0885-8977, 25(3): 1268-1274.
Wen, F.S.; Han, Z.X. (1995); Fault Section Estimation in Power Systems Using a Genetic Algorithm," Electr. Pow. Syst.s Res., ISSN 0378-7796, 34(3): 165-172.
The Matlab Website. http://www.mathworks.es/products/matlab/.
Research Group on Natural Computing, University of Seville: The P-Lingua Website. http://www.p-lingua.org.
Research Group on Natural Computing, University of Seville: The MeCoSim Website. http://www.p-lingua.org/mecosim.
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