Encyclopedia of Complexity and Systems Science

Living Edition
| Editors: Robert A. Meyers

Applications of P Systems

  • Marian GheorgheEmail author
  • Andrei Păun
  • Sergey Verlan
  • Gexiang Zhang
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27737-5_698-1
  • 93 Downloads

Glossary

Computational model

A computational model is a concept introduced in computer science with the aim of defining an algorithm that is executed on an abstract machine. It is built for different purposes and makes use of various notations and formalisms. Some of the most widely used computational models are finite state machines, Turing machines, formal grammars, Boolean networks, Petri nets, cellular automata, and process calculi.

Execution strategy of a P system

Every P system is executed in steps. In each step and each compartment, a number of rules are selected to be applied to the multiset contained in the compartment. The most utilized execution strategies are maximal parallelism (in each compartment after the rules are selected, no more objects are available to be processed by the existing rules), sequential execution (only one rule per compartment is applied), and stochastic behavior (the rules are selected in accordance with the probabilities associated to them). In most...

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Notes

Acknowledgments

The work of G. Zhang was supported by the National Natural Science Foundation of China (61373047 and 61672437) and the Research Project of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, P. R. China (JYBFXYQ-1).

Bibliography

Primary Literature

  1. Bartocci E, Lió (2016) Computational modeling, formal analysis, and tools for systems biology. PLoS Comput Biol 21(1):e1004,591CrossRefGoogle Scholar
  2. Buiu C, Vasile CI, Arsene O (2012) Development of membrane controllers for mobile robots. Inf Sci 187:33–51CrossRefGoogle Scholar
  3. Ciobanu G, Păun Gh, Pérez-Jiménez MJ (eds) (2006) Applications of membrane computing. Natural computing series. Springer, BerlinGoogle Scholar
  4. Fisher J, Henzinger T (2007) Executable cell biology. Nat Biotechnol 25(11):1239–1249CrossRefGoogle Scholar
  5. Frisco P, Gheorghe M, Pérez-Jiménez MJ (eds) (2014) Applications of membrane computing in systems and synthetic biology. Emergence, complexity and computation. Springer, ChamGoogle Scholar
  6. Gheorghe M, Păun G, Pérez-Jiménez MJ, Rozenberg G (2013) Research frontiers of membrane computing: open problems and research topics. Int J Found Comput Sci 24(5):547–624MathSciNetCrossRefzbMATHGoogle Scholar
  7. Nishida TY (2004) An application of P system: a new algorithm for NP-complete optimization problems. In: Proceedings of the 8th world multi-conference on systems, cybernetics and informatics, vol 5, pp 109–112Google Scholar
  8. Păun G (2000) Computing with membranes. J Comput Syst Sci 61(1):108–143. also Turku Center for Computer Science Report TUCS 208, Nov 1998MathSciNetCrossRefzbMATHGoogle Scholar
  9. Păun GH (2012) Membrane computing. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Berlin, pp 1355–1377CrossRefGoogle Scholar
  10. Păun G, Rozenberg G, Salomaa A (eds) (2010a) The Oxford handbook of membrane computing. Oxford University Press, OxfordzbMATHGoogle Scholar
  11. Peng H, Wang J, Pérez-Jiménez MJ, Wang H, Shao J, Wang T (2013) Fuzzy reasoning spiking neural P system for fault diagnosis. Inf Sci 235:106–116MathSciNetCrossRefzbMATHGoogle Scholar
  12. Wang T, Zhang G, Pérez-Jiménez MJ (2015a) Fuzzy membrane computing: theory and applications. Int J Comput Commun 10:904–935Google Scholar
  13. Wang T, Zhang G, Zhao J, He Z, Zhao J, Wang J, Pérez-Jiménez MJ (2015b) Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE T Power Syst 30(3):1182–1194CrossRefGoogle Scholar
  14. Wang X, Zhang G, Neri F, Jiang T, Zhao J, Gheorghe M, Ipate F, Lefticaru R (2015c) Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots. Integr Comput Aided Eng 23(1):15–30CrossRefGoogle Scholar
  15. 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. Appl Soft Comput 13(3):1528–1542CrossRefGoogle Scholar
  16. Zhang G, Cheng J, Gheorghe M (2014a) Dynamic behavior analysis of membrane-inspired evolutionary algorithms. Int J Comput Commun 9(2):227–242ADSCrossRefGoogle Scholar
  17. Zhang G, Gheorghe M, Pan L, Pérez-Jiménez MJ (2014b) Evolutionary membrane computing: a comprehensive survey and new results. Inf Sci 279:528–551CrossRefGoogle Scholar
  18. Zhang G, Rong H, Neri F, Pérez-Jiménez MJ (2014c) An optimization spiking neural P system for approximately solving combinatorial optimization problems. Int J Neural Syst 24(5):1–16CrossRefGoogle Scholar
  19. Zhang G, Pérez-Jiménez MJ, Gheorghe M (2017) Real-life applications with membrane computing. Emergence, complexity and computation. Springer, ChamGoogle Scholar

Books and Reviews

  1. del Amor MAM, García-Quismondo M, Macías-Ramos LF, Valencia-Cabrera L, Nez ARN, Pérez-Jiménez MJ (2015) Simulating P systems on GPU devices: a survey. Fundam Inform 136(3):269–284MathSciNetzbMATHGoogle Scholar
  2. Dinneen MJ, Kim YB, Nicolescu R (2012) Faster synchronization in P systems. Nat Comput 11(1):107–115MathSciNetCrossRefzbMATHGoogle Scholar
  3. Freund R, Păun G, Rozenberg G, Salomaa A (eds) (2006) Membrane computing, 6th international workshop, WMC 2005, Vienna, 18–21 July 2005, Revised selected and invited papers, Lecture notes in computer science, vol 3850, SpringerGoogle Scholar
  4. Leporati A, Rozenberg G, Salomaa A, Zandron C (eds) (2017) Membrane computing – 17th international conference, CMC 2016, Milan, 25–29 July 2016, Revised selected papers, Lecture notes in computer science, vol 10105, SpringerGoogle Scholar
  5. Macías-Ramos LF, Valencia-Cabrera L, Song B, Song T, Pan L, Pérez-Jiménez MJ (2015) A P_Lingua based simulator for P systems with symport/antiport rules. Fundam Inform 139(2):211–227CrossRefzbMATHGoogle Scholar
  6. Manca V (2013) Infobiotics – information in biotic systems. Emergence, complexity and computation. Springer, HeidelbergGoogle Scholar
  7. Nicolescu R, Ipate F, Wu H (2013) Programming P systems with complex objects. In: Alhazov A, Cojocaru S, Gheorghe M, Rogohzin Y, Rozenberg G, Salomaa A (eds) Membrane computing, international conference, CMC 2013, Chişninău, 20–23 Aug 2013, Revised papers, Springer, Lecture notes in computer science, vol 8340, pp 280–300Google Scholar
  8. Păun GH, Pérez-Jiménez MJ, Riscos-Núñez A, Rozenberg G, Salomaa A (eds) (2010b) Membrane computing, 10th international workshop, WMC 2009, Curtea de Arges, 24–27 Aug 2009. Revised selected and invited papers, Lecture notes in computer science, vol 5957, SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Marian Gheorghe
    • 1
    Email author
  • Andrei Păun
    • 2
  • Sergey Verlan
    • 3
  • Gexiang Zhang
    • 4
    • 5
    • 6
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of BradfordBradfordUK
  2. 2.Department of Computer ScienceUniversity of BucharestBucharestRomania
  3. 3.LACLUniversité Paris Est CréteilCréteilFrance
  4. 4.Robotics Research CenterXihua UniversityChengduChina
  5. 5.China Key Laboratory of Fluid and Power MachineryXihua University, Ministry of EducationChengduChina
  6. 6.School of Electrical EngeneeringSouthwest Jiaotong UniversityChengduChina