Skip to main content
SpringerLink
Log in

A bibliometric analysis of membrane computing (1998–2019)

  • Survey Paper
  • Published:
Journal of Membrane Computing Aims and scope Submit manuscript

Abstract

Membrane computing as a topic in the Mathematics Subjects Classification 2020 by Mathematical Reviews and zbMATH is a sign of maturity of this research area initiated in the fall of 1998 by Gheorghe Păun. This paper uses CiteSpace and VoSviewer to make a bibliometric analysis of membrane computing based on the publications in more than twenty widely used databases from 1998 to 2019 to describe the state of the art of membrane computing research. The aim is to present a comprehensive overview of the main influencing factors affecting the development of membrane computing and to identify the most influential publications, authors, citation structures, ground-breaking research directions and milestone events in the membrane computing community. Three international flagship conferences on membrane computing, special issues of international journals, books and projects are analyzed. The publications and general citation structure of the journals as well as the most cited articles are discussed. The authorship and co-authorship, the maps of the author co-citation network and representative co-authorship sub-networks are investigated. The countries involved in membrane computing and document co-citation analysis, the most influential and productive countries are presented. Finally, the co-occurrence network for exploring the ground-breaking research topics is generated and the main co-citation clusters of membrane computing publications are discovered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Notes

  1. http://psystems.disco.unimib.it/.

  2. http://ppage.psystems.eu/.

  3. http://membranecomputing.net/IMCSBulletin/.

References

  1. Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics. Annual Review of Information Science and Technology, 36(1), 2–72.

    Article  Google Scholar 

  2. Claude, R., Charles-Daniel, A., Jean, A., & Jean-Francois, G. (2004). Bibliometric overview of the utilization of artificial neural networks in medicine and biology. Scientometrics, 59(1), 117–130.

    Article  Google Scholar 

  3. Alvarez-Betancourt, Y., & Garcia-Silvente, M. (2014). An overview of iris recognition: A bibliometric analysis of the period 2000–2012. Scientometrics, 101(3), 2003–2033.

    Article  Google Scholar 

  4. Yu, D., Xu, Z., Kao, Y., & Lin, C.-T. (2017). The structure and citation landscape of IEEE transactions on fuzzy systems (1994–2015). IEEE Transactions on Fuzzy Systems, 26(2), 430–442.

    Article  Google Scholar 

  5. Wang, X., Xu, Z., & Dzitac, I. (2019). Bibliometric analysis on research trends of international journal of computers communications & control. International Journal of Computers, Communications & Control 14(5)

  6. Adams, W. C., Lind Infeld, D., Wikrent, K. L., & Bintou Cisse, O. (2016). Network bibliometrics of public policy journals. Policy Studies Journal, 44(S1), 133–151.

    Article  Google Scholar 

  7. Maditati, D. R., Munim, Z. H., Schramm, H.-J., & Kummer, S. (2018). A review of green supply chain management: From bibliometric analysis to a conceptual framework and future research directions. Resources, Conservation and Recycling, 139, 150–162.

    Article  Google Scholar 

  8. Liang, T.-P., & Liu, Y.-H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10.

    Article  Google Scholar 

  9. Garg, K. C., Kumar, S., Madhavi, Y., & Bahl, M. (2009). Bibliometrics of global malaria vaccine research. Health Information & Libraries Journal, 26(1), 22–31.

    Article  Google Scholar 

  10. Chen, H., & Ho, Y.-S. (2015). Highly cited articles in biomass research: A bibliometric analysis. Renewable and Sustainable Energy Reviews, 49, 12–20.

    Article  Google Scholar 

  11. Jiang, H., Qiang, M., & Lin, P. (2016). A topic modeling based bibliometric exploration of hydropower research. Renewable and Sustainable Energy Reviews, 57, 226–237.

    Article  Google Scholar 

  12. Păun, G. (2000). Computing with membranes. Journal of Computer and System Sciences, 61(1), 108–143.

    Article  MathSciNet  MATH  Google Scholar 

  13. Păun, G. (2010). A quick introduction to membrane computing. The Journal of Logic and Algebraic Programming, 79(6), 291–294.

    Article  MathSciNet  MATH  Google Scholar 

  14. Zhang, G., Pérez-Jiménez, M. J., Riscos-Núñez, A., Verlan, S., Konur, S., Hinze, T., & Gheorghe, M. (2021). Membrane computing models: Implementations. Springer.

    Book  Google Scholar 

  15. Pan, L., Păun, G., & Zhang, G. (2019). Foreword: Starting JMC. Journal of Membrane Computing, 1(1), 1–2.

    Article  Google Scholar 

  16. Păun, G.R., Grzegorz, & Salomaa, A. Eds. (2010). The Oxford handbook of membrane computing. Oxford University Press

  17. Seuring, S., Müller, M., Westhaus, M., & Morana, R. (2005). Conducting a literature review-the example of sustainability in supply chains. Research methodologies in supply chain management, 91–106

  18. Chen, C. (2006). Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

    Article  Google Scholar 

  19. Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in citespace. Expert Opinion on Biological Therapy, 12(5), 593–608.

    Article  Google Scholar 

  20. Brocke, J.v., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process

  21. Brocke, J.v., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R., & Cleven, A. (2009).Reconstructing the giant: On the importance of rigour in documenting the literature search process

  22. Păun, G. (2001). Computing with membranes: Attacking NP-complete problems. In: Unconventional Models of Computation, UMC’2K, pp. 94–115. Springer.

  23. Păun, G. (2001). P systems with active membranes: Attacking NP complete problems. Journal of Automata, Languages and Combinatorics, 6(1), 75–90.

    MathSciNet  MATH  Google Scholar 

  24. Păun, G. (2002). Membrane computing: An introduction. Springer.

    Book  MATH  Google Scholar 

  25. Romero-Jiménez, Á., & Pérez-Jiménez, M. J. (2002). Simulating turing machines by P systems with external output. Fundamenta Informaticae, 49(1–3), 273–287.

    MathSciNet  MATH  Google Scholar 

  26. Păun, A., & Păun, G. (2002). The power of communication: P systems with symport/antiport. New Generation Computing, 20(3), 295–305.

    Article  MATH  Google Scholar 

  27. Csuhaj-Varjú, E. (2004). P automata. In: International Workshop on Membrane Computing, pp. 19–35. Springer

  28. Pérez-Jiménez, M. J., Jiménez, Á. R., & Caparrini, F. S. (2003). Complexity classes in models of cellular computing with membranes. Natural Computing, 2(3), 265–285.

    Article  MathSciNet  MATH  Google Scholar 

  29. Calude, C. S., & Păun, G. (2004). Bio-steps beyond turing. Bio Systems, 77(1–3), 175–194.

    Article  Google Scholar 

  30. Gheorghe, M., & Stannett, M. (2012). Membrane system models for super-turing paradigms. Natural Computing, 11(2), 253–259.

    Article  MathSciNet  MATH  Google Scholar 

  31. Bernardini, F., & Gheorghe, M. (2004). Population P systems. Journal of Universal Computer Science, 10(5), 509–539.

    MathSciNet  Google Scholar 

  32. Kelemen, J., Kelemenová, A., & Păun, G. (2004). Preview of p colonies: A biochemically inspired computing model. In: Workshop and Tutorial Proceedings., pp. 82–86. Boston Mass

  33. Nishida, T.Y. (2004). An application of P-system: A new algorithm for NP-complete optimization problems. In: The 8th World Multi-Conference on Systems, Cybernetics and Informatics, vol. 5, pp. 109–112

  34. Pérez-Jiménez, M.J., & Romero-Campero, F.J. (2005). A study of the robustness of the egfr signalling cascade using continuous membrane systems. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 268–278 . Springer

  35. Ionescu, M., Păun, G., & Yokomori, T. (2006). Spiking neural P systems. Fundamenta Informaticae, 71(2,3), 279–308.

    MathSciNet  MATH  Google Scholar 

  36. Păun, G., & Păun, R. (2006). Membrane computing and economics: Numerical P systems. Fundamenta Informaticae, 73(1,2), 213–227.

    MathSciNet  MATH  Google Scholar 

  37. Ciobanu, G., & Gontineac, M. (2006). P machines: An automata approach to membrane computing. In: International Workshop on Membrane Computing, pp. 314–329. Springer

  38. Corne, D., Frisco, P., Păun, G., Rozenberg, G., & Salomaa, A. (2009). Membrane Computing: 9th International Workshop, WMC 2008, Edinburgh, UK, July 28-31, 2008 vol. 5391. Springer.

  39. Díaz-Pernil, D., Pérez-Hurtado, I., Pérez-Jiménez, M.J., & Riscos-Núnez, A. (2008). A p-lingua programming environment for membrane computing. In: International Workshop on Membrane Computing, pp. 187–203. Springer

  40. Zhang, G., Gheorghe, M., & Wu, C. (2008). A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fundamenta Informaticae, 87(1), 93–116.

    MathSciNet  MATH  Google Scholar 

  41. Frisco, P. (2009). Computing with cells: Advances in membrane computing. OUP Oxford.

    Book  MATH  Google Scholar 

  42. Pérez-Hurtado, I., Valencia-Cabrera, L., Pérez-Jiménez, M.J., Colomer, M.A., & Riscos-Núñez, A. (2010). Mecosim: A general purpose software tool for simulating biological phenomena by means of P systems. In: 2010 IEEE Fifth International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA), pp. 637–643. IEEE

  43. Zhang, G., & Pan, L. (2010). A survey of membrane computing as a new branch of natural computing. Chinese Journal of Computers, 33(2), 208–214.

    Article  Google Scholar 

  44. Gheorghe, M., Păun, G., & Pérez-Jiménez, M. J. (2013). Research frontiers of membrane computing: Open problems and research topics. International Journal of Foundations of Computer Science, 24(5), 547–623.

    Article  MathSciNet  MATH  Google Scholar 

  45. Peng, H., Wang, J., Pérez-Jiménez, M. J., Wang, H., Shao, J., & Wang, T. (2013). Fuzzy reasoning spiking neural P system for fault diagnosis. Information Sciences, 235, 106–116.

    Article  MathSciNet  MATH  Google Scholar 

  46. Frisco, P., Gheorghe, M., & Pérez-Jiménez, M. J. (2014). Applications of membrane computing in systems and synthetic biology. Springer.

    Book  Google Scholar 

  47. Zhang, G., Rong, H., Neri, F., & Pérez-Jiménez, M. J. (2014). An optimization spiking neural P system for approximately solving combinatorial optimization problems. International Journal of Neural Systems, 24(05), 1440006.

    Article  Google Scholar 

  48. Zhang, G., Cheng, J., Wang, T., Wang, X., & Zhu, J. (2015). Membrane computing: Theory and applications. Science Press.

    Google Scholar 

  49. Zhang, G., Pérez-Jiménez, M. J., & Gheorghe, M. (2017). Real-life applications with membrane computing. Springer.

    Book  MATH  Google Scholar 

  50. https://www.springer.com/journal/41965 (2019)

  51. Zhang, G., Shang, Z., Verlan, M. A. S., Martínez-Del-Amor, Y., Valencia-Cabrera, L.C., & Pérez-Jiménez, M.J. (2020). An overview of hardware implementation of membrane computing models. ACM Computing Surveys, 53(4), 1–38.

    Google Scholar 

  52. Archambault, É., Beauchesne, O.H., & Caruso, J. (2011). Towards a multilingual, comprehensive and open scientific journal ontology. In: Proceedings of the 13th International Conference of the International Society for Scientometrics and Informetrics, pp. 66–77. Durban South Africa

  53. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000918 (2021)

  54. Krishna, S.-N.(2001). Languages of P systems: Computability and complexity. PhD thesis, Indian Institute of Technology Madras

  55. Montoro, F.A. (2004). Structures and biolanguage to simulate membrane computing. PhD thesis, Univeridad Politecnica de Madrid, Madrid, Spain

  56. Riscos-Núnez, A. (2004). Cellular programming: Efficient resolution of np–complete numerical problems. PhD thesis, Universidad de Sevilla, Sevilla, Spain

  57. Oswald, M. (2003). P automata. PhD thesis, Vienna University of Technology, Austria

  58. Besozzi, D. (2004). Computational and modelling power of p systems. PhD thesis, Universita’ degli Studi di Milano, Italy

  59. Frisco, P. (2004). Theory of molecular computing: Splicing and membrane systems. PhD thesis, Leiden University, The Netherlands

  60. Lakshmanan, L. (2003). On the crossroads of P systems and contextual grammars: Variants, computability, complexity and efficiency. PhD thesis, Indian Institute of Technology, Madras, India

  61. Verlan, S. (2004). Head systems and applications to bio-informatics. PhD thesis, Université de Metz, France

  62. Chan, Y.-H.M., & Boxer, S. G. (2007). Model membrane systems and their applications. Current Opinion in Chemical Biology, 11(6), 581–587.

    Article  Google Scholar 

  63. Martín-Vide, C., Păun, G., Pazos, J., Rodríguez-Patón, A. (2003). Tissue P systems. Theoretical Computer Science 296(2), 295–326

  64. Păun, G. (1999). P systems with active membranes: Attacking NP complete problems. Technical report, Department of Computer Science. New Zealand: The University of Auckland.

    Google Scholar 

  65. Păun, G., & Rozenberg, G. (2002). A guide to membrane computing. Theoretical Computer Science, 287(1), 73–100.

    Article  MathSciNet  MATH  Google Scholar 

  66. Song, T., Pan, L., & Păun, G. (2013). Asynchronous spiking neural P systems with local synchronization. Information Sciences, 219, 197–207.

    Article  MathSciNet  MATH  Google Scholar 

  67. Păun, A., & Păun, G. (2007). Small universal spiking neural P systems. Bio Systems, 90(1), 48–60.

    Article  MATH  Google Scholar 

  68. Pan, L., & Păun, G. (2009). Spiking neural P systems with anti-spikes. International Journal of Computers Communications & Control, 4(3), 273–282.

    Article  Google Scholar 

  69. Pan, L., & Pérez-Jiménez, M. J. (2010). Computational complexity of tissue-like P systems. Journal of Complexity, 26(3), 296–315.

    Article  MathSciNet  MATH  Google Scholar 

  70. Lucanu, D. (2009). Strategy-based rewrite semantics for membrane systems preserves maximal concurrency of evolution rule actions. Electronic Notes in Theoretical Computer Science, 237, 107–125.

    Article  MATH  Google Scholar 

  71. Zhang, G., Cheng, J., Gheorghe, M., & Meng, Q. (2013). A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 13(3), 1528–1542.

    Article  Google Scholar 

  72. Freund, R., Păun, G., & Pérez-Jiménez, M. J. (2005). Tissue P systems with channel states. Theoretical Computer Science, 330(1), 101–116.

    Article  MathSciNet  MATH  Google Scholar 

  73. Chen, H., Freund, R., Ionescu, M., Păun, G., & Pérez-Jiménez, M. J. (2007). On string languages generated by spiking neural P systems. Fundamenta Informaticae, 75(1–4), 141–162.

    MathSciNet  MATH  Google Scholar 

  74. Pan, L., Păun, G., & Pérez-Jiménez, M. J. (2011). Spiking neural P systems with neuron division and budding. Science China Information Sciences, 54(8), 1596–1607.

    Article  MathSciNet  MATH  Google Scholar 

  75. Wang, T., Zhang, G., Zhao, J., He, Z., Wang, J., & Pérez-Jiménez, M. J. (2014). Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Transactions on Power Systems, 30(3), 1182–1194.

    Article  Google Scholar 

  76. Zhang, X., Pan, L., & Păun, A. (2015). On the universality of axon P systems. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2816–2829.

    Article  MathSciNet  Google Scholar 

  77. Wang, J., Hoogeboom, H. J., Pan, L., Păun, G., & Pérez-Jiménez, M. J. (2010). Spiking neural P systems with weights. Neural Computation, 22(10), 2615–2646.

    Article  MathSciNet  MATH  Google Scholar 

  78. Ishdorj, T.-O., Leporati, A., Pan, L., Zeng, X., & Zhang, X. (2010). Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources. Theoretical Computer Science, 411(25), 2345–2358.

    Article  MathSciNet  MATH  Google Scholar 

  79. Wang, J., Shi, P., Peng, H., Pérez-Jiménez, M. J., & Wang, T. (2013). Weighted fuzzy spiking neural P systems. IEEE Transactions on Fuzzy Systems, 21(2), 209–220.

    Article  Google Scholar 

  80. Zandron, C., Ferretti, C., & Mauri, G. (2001). Solving NP-complete problems using P systems with active membranes. In: Unconventional Models of Computation, UMC’2K, pp. 289–301. Springer.

  81. Păun, G., Pérez-Jiménez, M. J., & Rozenberg, G. (2006). Spike trains in spiking neural P systems. International Journal of Foundations of Computer Science, 17(04), 975–1002.

    Article  MathSciNet  MATH  Google Scholar 

  82. Huang, L., Suh, I. H., & Abraham, A. (2011). Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants. Information Sciences, 181(11), 2370–2391.

    Article  Google Scholar 

  83. Cavaliere, M., Ibarra, O. H., Păun, G., Egecioglu, O., Ionescu, M., & Woodworth, S. (2009). Asynchronous spiking neural P systems. Theoretical Computer Science, 410(24–25), 2352–2364.

    Article  MathSciNet  MATH  Google Scholar 

  84. Pan, L., & Martin-Vide, C. (2005). Solving multidimensional 0–1 knapsack problem by P systems with input and active membranes. Journal of Parallel and Distributed Computing, 65(12), 1578–1584.

    Article  MATH  Google Scholar 

  85. Păun, G., Pérez-Jiménez, M. J., & Riscos Núñez, A. (2008). Tissue p systems with cell division. International Journal of Computers, Communications and Control, 3(3), 295–303.

    Article  Google Scholar 

  86. Song, T., Pan, L., Wang, J., Venkat, I., Subramanian, K., & Abdullah, R. (2012). Normal forms of spiking neural P systems with anti-spikes. IEEE Transactions on Nanobioscience, 11(4), 352–359.

    Article  Google Scholar 

  87. Song, T., & Pan, L. (2014). Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy. IEEE Transactions on Nanobioscience, 14(1), 38–44.

    Article  Google Scholar 

  88. Zeng, X., Zhang, X., Song, T., & Pan, L. (2014). Spiking neural P systems with thresholds. Neural Computation, 26(7), 1340–1361.

    Article  MathSciNet  MATH  Google Scholar 

  89. Alhazov, A., Martín-Vide, C., & Pan, L. (2003). Solving a P space-complete problem by recognizing P systems with restricted active membranes. Fundamenta Informaticae, 58(2), 67–77.

    MathSciNet  MATH  Google Scholar 

  90. Song, T., & Pan, L. (2015). Spiking neural P systems with rules on synapses working in maximum spiking strategy. IEEE Transactions on Nanobioscience, 14(4), 465–477.

    Article  Google Scholar 

  91. Pan, L., Wang, J., & Hoogeboom, H. J. (2012). Spiking neural P systems with astrocytes. Neural Computation, 24(3), 805–825.

    Article  MathSciNet  MATH  Google Scholar 

  92. Pan, L., & Păun, G. (2010). Spiking neural P systems: An improved normal form. Theoretical Computer Science, 411(6), 906–918.

    Article  MathSciNet  MATH  Google Scholar 

  93. Pescini, D., Besozzi, D., Mauri, G., & Zandron, C. (2006). Dynamical probabilistic P systems. International Journal of Foundations of Computer Science, 17(01), 183–204.

    Article  MathSciNet  MATH  Google Scholar 

  94. Ciobanu, G., Pan, L., Păun, G., & Pérez-Jiménez, M. J. (2007). P systems with minimal parallelism. Theoretical Computer Science, 378(1), 117–130.

    Article  MathSciNet  MATH  Google Scholar 

  95. Pan, L., Zeng, X., & Zhang, X. (2011). Time-free spiking neural P systems. Neural Computation, 23(5), 1320–1342.

    Article  MathSciNet  MATH  Google Scholar 

  96. Zhang, G., Gheorghe, M., Pan, L., & Pérez-Jiménez, M. J. (2014). Evolutionary membrane computing: A comprehensive survey and new results. Information Sciences, 279, 528–551.

    Article  Google Scholar 

  97. Zhao, R., & Ju, W. (2011). Visualizing the research on pervasive and ubiquitous computing. Scientometrics, 86(3), 593–612.

    Article  Google Scholar 

  98. Niazi, M., & Hussain, A. (2011). Agent-based computing from multi-agent systems to agent-based models: A visual survey. Scientometrics, 89(2), 479–499.

    Article  Google Scholar 

  99. Van Eck, N. J., & Waltman, L. (2010). Software survey: Vosviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  100. Small, H. (1980). Co-citation context analysis and the structure of paradigms. Journal of Documentation

  101. Small, H. (2003). Paradigms, citations, and maps of science: A personal history. Journal of the American Society for Information Science and Technology, 54(5), 394–399.

    Article  Google Scholar 

  102. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.

    Article  MATH  Google Scholar 

  103. Freeman, L.C. (1977). A set of measures of centrality based on betweenness. Sociometry, 35–41

  104. Pan, L., & Zeng, X. (2011). Small universal spiking neural P systems working in exhaustive mode. IEEE Transactions on Nanobioscience, 10(2), 99–105.

    Article  Google Scholar 

  105. Ibarra, O. H., Păun, A., & Rodríguez-Patón, A. (2009). Sequential SNP systems based on min/max spike number. Theoretical Computer Science, 410(30–32), 2982–2991.

    Article  MathSciNet  MATH  Google Scholar 

  106. Wang, J., Zhou, L., Peng, H., & Zhang, G. (2011). An extended spiking neural P system for fuzzy knowledge representation. International Journal of Innovative Computing, Information and Control, 7(7), 3709–3724.

    Google Scholar 

  107. Peng, H., Wang, J., Pérez-Jiménez, M. J., & Riscos-Núñez, A. (2015). An unsupervised learning algorithm for membrane computing. Information Sciences, 304, 80–91.

    Article  MATH  Google Scholar 

  108. Pan, L., & Alhazov, A. (2006). Solving HPP and SAT by P systems with active membranes and separation rules. Acta Informatica, 43(2), 131–145.

    Article  MathSciNet  MATH  Google Scholar 

  109. Nishida, T.Y. (2005). Membrane algorithms. In: International Workshop on Membrane Computing, pp. 55–66. Springer

  110. Muniyandi, R. C., & Maroosi, A. (2015). Enhancing the simulation of membrane system on the GPU for the n-queens problem. Chinese Journal of Electronics, 24(4), 740–743.

    Article  Google Scholar 

  111. Guo, P., Chen, H., & Zheng, H. (2014). Arithmetic expression evaluations with membranes. Chinese Journal of Electronics, 23(1), 55–60.

    Google Scholar 

  112. Aman, B., & Ciobanu, G. (2019). Synchronization of rules in membrane computing. Journal of Membrane Computing, 1(4), 233–240.

    Article  MathSciNet  MATH  Google Scholar 

  113. Bottoni, P., Labella, A., & Rozenberg, G. (2019). Reaction systems with influence on environment. Journal of Membrane Computing, 1(1), 3–19.

    Article  MathSciNet  MATH  Google Scholar 

  114. Buiu, C., & Florea, A. G. (2019). Membrane computing models and robot controller design, current results and challenges. Journal of Membrane Computing, 1(4), 262–269.

    Article  MathSciNet  MATH  Google Scholar 

  115. Cooper, J., & Nicolescu, R. (2019). Alternative representations of P systems solutions to the graph colouring problem. Journal of Membrane Computing, 1(2), 112–126.

    Article  MathSciNet  MATH  Google Scholar 

  116. de la Cruz, R. T. A., Cabarle, F. G., & Adorna, H. N. (2019). Generating context-free languages using spiking neural P systems with structural plasticity. Journal of Membrane Computing, 1(3), 161–177.

    Article  MathSciNet  MATH  Google Scholar 

  117. Díaz-Pernil, D., Gutiérrez-Naranjo, M. A., & Peng, H. (2019). Membrane computing and image processing: A short survey. Journal of Membrane Computing, 1(1), 58–73.

    Article  MathSciNet  Google Scholar 

  118. Gazdag, Z., & Kolonits, G. (2019). A new method to simulate restricted variants of polarizationless P systems with active membranes. Journal of Membrane Computing, 1(4), 251–261.

    Article  MathSciNet  MATH  Google Scholar 

  119. Jimenez, Z. B., Cabarle, F. G. C., de la Cruz, R. T. A., Buño, K. C., Adorna, H. N., Hernandez, N. H. S., & Zeng, X. (2019). Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity. Journal of Membrane Computing, 1(3), 145–160.

    Article  MathSciNet  MATH  Google Scholar 

  120. Leporati, A., Manzoni, L., Mauri, G., Porreca, A. E., & Zandron, C. (2019). Characterizing P space with shallow non-confluent P systems. Journal of Membrane Computing, 1(2), 75–84.

    Article  MathSciNet  MATH  Google Scholar 

  121. Mayne, R., Phillips, N., & Adamatzky, A. (2019). Towards experimental p-systems using multivesicular liposomes. Journal of Membrane Computing, 1(1), 20–28.

    Article  MathSciNet  Google Scholar 

  122. Nash, A., & Kalvala, S. (2019). AP system model of swarming and aggregation in a myxobacterial colony. Journal of Membrane Computing, 1(2), 103–111.

    Article  MathSciNet  Google Scholar 

  123. Orellana-Martín, D., Valencia-Cabrera, L., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2019). P systems with proteins: A new frontier when membrane division disappears. Journal of Membrane Computing, 1(1), 29–39.

    Article  MathSciNet  MATH  Google Scholar 

  124. Orellana-Martín, D., Valencia-Cabrera, L., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2019). Minimal cooperation as a way to achieve the efficiency in cell-like membrane systems. Journal of Membrane Computing, 1(2), 85–92.

    Article  MathSciNet  MATH  Google Scholar 

  125. Pérez-Hurtado, Ignacio, Orellana-Martín, D., Zhang, G., & Pérez-Jiménez, M. (2019). P-lingua in two steps: Flexibility and efficiency. Journal of Membrane Computing, 1(2), 93–102.

    Article  MathSciNet  Google Scholar 

  126. Román, G. (2019). Inference of bounded l systems with polymorphic P systems. Journal of Membrane Computing, 1(1), 52–57.

    Article  MathSciNet  MATH  Google Scholar 

  127. Sánchez-Karhunen, E., & Valencia-Cabrera, L. (2019). Modelling complex market interactions using PDP systems. Journal of Membrane Computing, 1(1), 40–51.

    Article  MathSciNet  Google Scholar 

  128. Valencia-Cabrera, L., Orellana-Martín, D., Martínez-del-Amor, M. Á., & Pérez-Jiménez, M. J. (2019). An interactive timeline of simulators in membrane computing. Journal of Membrane Computing, 1(3), 209–222.

    Article  MathSciNet  Google Scholar 

  129. Sosík, P. (2019). P systems attacking hard problems beyond NP: A survey. Journal of Membrane Computing, 1(3), 198–208.

    Article  MathSciNet  MATH  Google Scholar 

  130. Zhang, G., Rong, H., Neri, F., & Pérez-Jiménez, M. J. (2021). A complete arithmetic calculator constructed from spiking neural P systems and its application to information fusion. International Journal of Neural Systems, 31(1), 1–17.

    Article  Google Scholar 

  131. Zhu, M., Yang, Q., Dong, J., Zhang, G., Gou, X., Rong, H., et al. (2021). An adaptive optimization spiking neural P system for binary problems. International Journal of Neural Systems, 31(1), 1–17.

    Article  Google Scholar 

  132. Zhang, G., Zhang, X., Rong, H., Paul, P., Zhu, M., Neri, F., & Ong, Y.-S. (2022). A layered spiking neural system for classification problems. International Journal of Neural Systems, 32, 1–16. https://doi.org/10.1142/S012906572250023X.

    Article  Google Scholar 

  133. Dong, J., Zhang, G., Luo, B., Yang, Q., Guo, D., Rong, H., et al. (2022). A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems. Information Sciences, 596, 1–14. https://doi.org/10.1016/j.ins.2022.03.007.

    Article  Google Scholar 

  134. Deng, X., Dong, J., Wang, S., Luo, B., Feng, H., & Zhang, G. (2022). Reducer lubrication optimization with an optimization spiking neural P system. Information Sciences, 604, 28–44. https://doi.org/10.1016/j.ins.2022.05.016.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61972324, 61672437, 61702428), the Sichuan Science and Technology Program (2021YFS0313, 2021YFG0133), the Artificial Intelligence Key Laboratory of Sichuan Province (2019RYJ06) and the Beijing Advanced Innovation Center for Intelligent Robots and Systems (2019IRS14).

Author information

Author notes

  1. Haina Rong and Yingying Duan contributed equally to this work.

Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Haina Rong, Yingying Duan & Gexiang Zhang

  2. School of Automation, Chengdu University of Information Technology, Chengdu, 610225, China

    Gexiang Zhang

Authors
  1. Haina Rong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingying Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gexiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gexiang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rong, H., Duan, Y. & Zhang, G. A bibliometric analysis of membrane computing (1998–2019). J Membr Comput 4, 177–207 (2022). https://doi.org/10.1007/s41965-022-00098-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41965-022-00098-2

Keywords

Search

Navigation