Fuzzy Membrane Computing: Theory and Applications

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

Proceedings of the Workshop on Membrane Computing, WMC 2016.

yesThis Workshop on Membrane Computing, at the Conference of Unconventional Computation and Natural Computation (UCNC), 12th July 2016, Manchester, UK, is the second event of this type after the Workshop at UCNC 2015 in Auckland, New Zealand*. Following the tradition of the 2015 Workshop the Proceedings are published as technical report. The Workshop consisted of one invited talk and six contributed presentations (three full papers and three extended abstracts) covering a broad spectrum of topics in Membrane Computing, from computational and complexity theory to formal verification, simulation and applications in robotics. All these papers – see below, but the last extended abstract, are included in this volume. The invited talk given by Rudolf Freund, “P SystemsWorking in Set Modes”, presented a general overview on basic topics in the theory of Membrane Computing as well as new developments and future research directions in this area. Radu Nicolescu in “Distributed and Parallel Dynamic Programming Algorithms Modelled on cP Systems” presented an interesting dynamic programming algorithm in a distributed and parallel setting based on P systems enriched with adequate data structure and programming concepts representation. Omar Belingheri, Antonio E. Porreca and Claudio Zandron showed in “P Systems with Hybrid Sets” that P systems with negative multiplicities of objects are less powerful than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov presented in “Extended Spiking Neural P Systems with States” new results regading the newly introduced topic of spiking neural P systems where states are considered. “Selection Criteria for Statistical Model Checker”, by Mehmet E. Bakir and Mike Stannett, presented some early experiments in selecting adequate statistical model checkers for biological systems modelled with P systems. In “Towards Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPU”, Raluca Lefticaru, Luis F. Macías-Ramos, Ionuţ M. Niculescu, Laurenţiu Mierlă presented some of the advatages of implementing kernel P systems simulations in FLAME. Andrei G. Florea and Cătălin Buiu, in “An Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots. *http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc- at-the-conference-on-unconventional-computation-natural-computation

New applications for an old tool

First, the dependency graph technique, not so far from its current application, was developed trying to nd the shortest computations for membrane systems solving instances of SAT. Certain families of membrane systems have been demonstrated to be non-effcient by means of the reduction of nding an accepting computation (respectively, rejecting computation) to the problem of reaching from a node of the dependency graph to another one. In this paper, a novel application to this technique is explained. Supposing that a problem can be solved by means of a kind of membrane systems leads to a contradiction by means of using the dependency graph as a reasoning method. In this case, it is demonstrated that a single system without dissolution, polarizations and cooperation cannot distinguish a single object from more than one object. An extended version of this work will be presented in the 20th International Conference on Membrane Computing.Ministerio de Industria, Economía y Competitividad TIN2017-89842-

Limits on P Systems with Proteins and Without Division

In the field of Membrane Computing, computational complexity theory has been widely studied trying to nd frontiers of efficiency by means of syntactic or semantical ingredients. The objective of this is to nd two kinds of systems, one non-efficient and another one, at least, presumably efficient, that is, that can solve NP-complete prob- lems in polynomial time, and adapt a solution of such a problem in the former. If it is possible, then P = NP. Several borderlines have been defi ned, and new characterizations of different types of membrane systems have been published. In this work, a certain type of P system, where proteins act as a supporting element for a rule to be red, is studied. In particular, while division rules, the abstraction of cellular mitosis is forbidden, only problems from class P can be solved, in contrast to the result obtained allowing them.Ministerio de Economía y Competitividad TIN2017-89842-PNational Natural Science Foundation of China No 6132010600

Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather dif cult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing.We rst model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain

Modeling of Decision Trees Through P systems

[EN] In this paper, we propose a decision-tree modeling in the framework of membrane computing. We propose an algorithm to obtain a P system that is equivalent to any decision tree taken as input. In our case, and unlike previous proposals, we formulate the concepts of decision trees endogenously, since there is no external agent involved in the modeling. The tree structure can be defined naturally by the topology of the regions in the P system and the decision rules are defined by communication rules of the P system.Sempere Luna, JM. (2019). Modeling of Decision Trees Through P systems. New Generation Computing. 37(3):325-337. https://doi.org/10.1007/s00354-019-00052-4325337373Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1984)Cardona, M., Colomer, M.A., Margalida, A., Palau, A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Sanuy, D.: A computational modeling for real ecosystems based on P systems. Nat. Comput. 10(1), 39–53 (2011)Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulation of P systems with active membranes on CUDA. Brief. Bioinform. 11(3), 313–322 (2010)Díaz-Pernil, D., Peña-Cantillana, F., Gutiérrez-Naranjo, M.A.: Self-constructing Recognizer P Systems. In: Proceedings of the Thirteenth Brainstorming Week on Membrane Computing. Fénix Editora, pp. 137–154 (2014)Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8, 87–102 (1992)Kingsford, C., Salzberg, S.L.: What are decision trees ? Nat. Biotechnol. 26(9), 1011–1013 (2008)Martín-Vide, C., Păun, Gh, Pazos, J., Rodríguez-Patón, A.: Tissue P systems. Theor. Comput. Sci. 296, 295–326 (2003)Martínez-del-Amor, M.A., García-Quismondo, M., Macías-Ramos, L.F., Valencia-Cabrera, L., Riscos-Núñez, A., Pérez-Jiménez, M.J.: Simulating P systems on GPU devices: a survey. Fund. Inf. 136(3), 269–284 (2015)Mitchell, T.: Machine Learning. McGraw-Hill, New York City (1997)Păun, Gh: Membrane Computing, An Introduction. Springer, Berlin (2002)Păun, Gh, Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)Sempere, J.M.: A View of P systems from information theory. In: Proceedings of the 17th international conference on membrane computing (CMC 2016) LNCS vol. 10105. Springer, pp. 352–362 (2017)Sammut, C., Webb, G.I. (eds.): Encyclopedia of Machine Learning. Springer, Berlin (2011)Wang, J., Hu, J., Peng, H., Pérez-Jiménez, M.J., Riscos-Núñez, A.: Decision tree models induced by membrane systems. Rom. J. Inf. Sci. Technol. 18(3), 228–239 (2015)Zhang, C., Ma, Y. (eds.): Ensemble Machine Learning, Methods and Applications. Springer, Berlin (2012)Zhang, X., Wang, B., Ding, Z., Tang, J., He, J.: Implementation of membrane algorithms on GPU. J. Appl. Math. 2014, 7 (2014

Membrane Systems and Petri Net Synthesis

Automated synthesis from behavioural specifications is an attractive and powerful way of constructing concurrent systems. Here we focus on the problem of synthesising a membrane system from a behavioural specification given in the form of a transition system which specifies the desired state space of the system to be constructed. We demonstrate how a Petri net solution to this problem, based on the notion of region of a transition system, yields a method of automated synthesis of membrane systems from state spaces.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347