Small Universal Numerical P Systems with Thresholds for Computing Functions

• Autorzy: Liu Liucheng , Yi Wenmei , Yang Qian , Peng Hong , Wang Jun

Identyfikatory

DOI

10.3233/FI-2020-1962

• Języki publikacji: EN

Abstrakty

EN

Abstracted from the nested structure of biological cells with application on the modeling of economical processes, numerical P systems (in short, NP systems) as a kind of distributed parallel computation systems have been proposed. It has been proven that NP systems and variants are Turing universal for number accepting/generating devices and language generating device. However, universality of NP systems as function computing devices has not been established. Aiming at numerical P systems with thresholds (in short, TNP systems), small universality for computing functions is discussed in this paper. Six small universal function computing devices of TNP systems for two threshold cases and working on three different modes are constructed, respectively.

Słowa kluczowe

EN

function computing devices; membrane computing; numerical P systems with thresholds; small universality

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2020
• Tom: Vol. 176, nr 1
• Strony: 43--59
• Opis fizyczny: Bibliogr. 57 poz., rys.

Twórcy

autor

Liu Liucheng

• School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China

autor

Yi Wenmei

• School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China

autor

Yang Qian

• School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China

autor

Peng Hong

• School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China

autor

Wang Jun

• School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China

Bibliografia

• [1] Alsalibi B, Venkat I, Al-Betar MA. A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios, Engineering Applications of Artificial Intelligence, 2017, 64:242-260. doi:10.1016/j.engappai.2017.06.018.
• [2] Bernardini F, Gheorghe M. Population P systems, Journal of Universal Computer Science, 2004, 10(5):509-539. http://www.jucs.org/jucs_10_5/population_p_systems.
• [3] Buiu C, Arsene O, Cipu C, Patrascu M. A software tool for modeling and simulation of numerical P systems, BioSystems, 2011, 103(3):442-447. doi:10.1016/j.biosystems.2010.11.013.
• [4] Buiu C, Vasile C, Arsene O. Development of membrane controllers for mobile robots, Information Sciences, 2012, 187:33-51. doi:10.1016/j.ins.2011.10.007.
• [5] Ciencialová L, Csuhaj-Varjú E, Kelemenová A, Vaszil G. Variants of P colonies with very simple cell structure, International Journal of Computers, Communications & Control, 2009, 4(3):224-233. doi:10.15837/ijccc.2009.3.2430.
• [6] Díaz-Pernil D, Berciano A, Peña-Cantillana F, Gutiérrez-Naranjo M.A. Segmenting images with gradient-based edge detection using membrane computing, Pattern Recognition Letters, 2013, 34(8):846-855. doi:10.1016/j.patrec.2012.10.014.
• [7] Freund R, Păun Gh, Pérez-Jiménez M.J. Tissue-like P systems with channel-states, Theoretical Computer Science, 2005, 330(1):101-116. doi:10.1016/j.tcs.2004.09.013.
• [8] García-Quismondo M, Levin M, Lobo-Fernández D. Modeling regenerative processes with membrane computing, Information Sciences, 2017, 381:229-249. doi:10.1016/j.ins.2016.11.017.
• [9] García-Quismondo M, Nisbet ICT, Mostello CS, Reed MJ. Modeling population dynamics of roseate terns (sterna dougallii) in the northwest Atlantic ocean, Ecological Modelling, 2018, 68:298-311.
• [10] Gheorghe M, Ipate F, Lefticaru R, Pérez-Jiménez MJ, et al. 3-Col problem modelling using simple kernel P systems, International Journal of Computer Mathematics, 2013, 90(4):816-830. doi:10.1080/00207160.2012.743712.
• [11] Gheorghe M, Manca V, Romero-Campero FJ. Deterministic and stochastic P systems for modelling cellular processes, Natural Computing, 2010, 9(2):457-473. doi:10.1007/s11047-009-9158-4.
• [12] Huang L, Suh IH, Abraham A. Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants, Information Sciences, 2011, 181(11):2370-2391.doi:10.1016/j.ins.2010.12.015.
• [13] Ionescu M, Păun Gh, Yokomori T. Spiking neural P systems, Fundamenta Informaticae, 2006, 71(2-3):279-308.
• [14] Korec I. Small universal register machines, Theoretical Computer Science, 1996, 168(2):267-301. doi:10.1016/S0304-3975(96)00080-1.
• [15] Leporati A, Porreca AE, Zandron C, Mauri G. Improving universality results on parallel enzymatic numerical P systems, International Journal of Unconventional Computing, 2013, 9(5):385-404. ID:5559554.
• [16] Li B, Peng H, Wang J, et al. Multi-focus image fusion based on dynamic threshold neural P systems and surfacelet transform, Knowledge-Based Systems, 2020, (196):1-12. doi:10.1016/j.knosys.2020.105794.
• [17] Li B, Peng H, Luo X, et al. Medical image fusion method based on coupled neural p systems in nonsubsampled shearlet transform domain, International Journal of Neural Systems, 2020. doi:10.1142/S0129065720500501.
• [18] Liu X, Xue J. A cluster splitting technique by Hopfield networks and P systems on simplices, Neural Processing Letters, 2017, 46(1):171-194. doi:10.1007/s11063-016-9577-z.
• [19] Z. Lv, T. Bao, N. Zhou, H. Peng, X. Huang, A. Riscos-Núñez, M.J. Pérez-Jiménez, Spiking neural p systems with extended channel rules, International Journal of Neural Systems, 2020. doi:10.1142/S0129065720500495.
• [20] Pan L, Zhang Z, Wu T, Xu J. Numerical P systems with production thresholds, 2017, 673:30-416. doi:10.1016/j.tcs.2017.02.026.
• [21] Păun Gh. Computing with membranes, Journal of Computer System Sciences, 2000, 61(1):108-143. doi:10.1006/jcss.1999.1693.
• [22] Păun Gh. Pérez-Jiménez MJ. Membrane computing: Brief introduction, recent results and applications, BioSystems, 2006, 85:11-22. doi:10.1016/j.biosystems.2006.02.001.
• [23] Păun Gh, Pérez-Jiménez M.J. Solving problems in a distributed way in membrane computing: dP systems, International Journal of Computers, Communications & Control, V(2), 2010, 238-250.
• [24] Păun Gh, Păun R. Membrance computing and economics: numberic P systems, Fundaments Informaticae, 2004, 73(1-2):213-227.
• [25] Păun Gh, Rozenberg G, Salomaa A. The Oxford Handbook of Membrane Computing, New York: Oxford University Press, Inc., 2010. ISBN:9780199556670.
• [26] Pavel AB, Arsene O, Buiu C. Enzymatic numerical P systems - a new class of membrane computing systems, IEEE Fifth International Conference on Bio-inspired Computing: Theories & Applications, 2010, pp. 1331-1336. doi:10.1109/BICTA.2010.5645071.
• [27] Pavel AB, Buiu C. Using enzymatic numerical P systems for modeling mobile robot controllers, Natural Computing, 2012, 11(3):387-393. doi:10.1007/s11047-011-9286-5.
• [28] Peng H, Bao T, Luo X, Wang J, Song X, Riscos-Núñez A, Pérez-Jiménez MJ. Dendrite P systems, Neural Networks, 2020, 127:110-120. doi:10.1016/j.neunet.2020.04.014.
• [29] Peng H, Chen R, Wang J, et al. Competitive spiking neural p systems with rules on synapses, IEEE Transactions on NanoBioscience, 2017, 16(8):888-895. doi:10.1109/TNB.2017.2783890.
• [30] Peng H, Li B, Wang J, Song X, Wang T, Valencia-Cabrera L, Pérez-Hurtado I, Riscos-Núñez A, Pérez-Jiménez MJ. Spiking neural P systems with inhibitory rules, Knowledge-Based Systems, 2020, 188:1-17. doi:10.1016/j.knosys.2019.105064.
• [31] Peng H, Lv Z, Li B, Luo X, Wang J, Song X, Wang T, Pérenz-Jiménez MJ, Riscos-Núñez A. Nonlinear spiking neural P systems, International Journal of Neural Systems, 2020. doi:10.1142/S0129065720500082.
• [32] Peng H, Shi P, Wang J, et al. Multiobjective fuzzy clustering approach based on tissue-like membrane systems, Knowledge-Based Systems, 2017, 125:74-82. doi:10.1016/j.knosys.2017.03.024.
• [33] Peng H, Wang J. Coupled neural P systems, IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(6):1672-1682. doi:10.1109/TNNLS.2018.2872999.
• [34] Peng H, Wang J, Ming J, et al. Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems, IEEE Transactions on Smart Grid, 2018, 9(5):4777-4784. doi:10.1109/TSG.2017.2670602.
• [35] Peng H, Wang J, Pérez-Jiménez MJ. Optimal multi-level thresholding with membrane computing, Digital Signal Processing, 2015, 37:53-64. doi:10.1016/j.dsp.2014.10.006.
• [36] Peng H, Wang J, Pérez-Jiménez MJ, et al. Fuzzy reasoning spiking neural P system for fault diagnosis, Information Sciences, 2013, 235(20):106-116. doi:10.1016/j.ins.2012.07.015.
• [37] Peng H, Wang J, Pérez-Jiménez MJ, et al. An unsupervised learning algorithm for membrane computing, Information Sciences, 2015, 304(20):80-91. doi:10.1016/j.ins.2015.01.019.
• [38] Peng H, Wang J, Pérez-Jiménez MJ, Riscos-Núñez A. The framework of P systems applied to solve optimal watermarking problem, Signal Processing, 2014, 101:256-265. doi:10.1016/j.sigpro.2014.02.020.
• [39] Peng H, Wang J, Pérez-Jiménez MJ, Riscos-Núñez A. Dynamic threshold neural P systems, Knowledge-Based Systems, 2019, 163:875-884. doi:10.1016/j.knosys.2018.10.016.
• [40] Peng H, Wang J, P Shi, et al. An extended membrane system with active membrane to solve automatic fuzzy clustering problems, International Journal of Neural Systems, 2016, 26(3):1-17. doi:10.1142/S0129065716500040.
• [41] Peng H, Wang J, Shi P, et al. Fault diagnosis of power systems using fuzzy tissue-like P systems, Integrated Computer-Aided Engineering, 2017, 24 (4): 401-411. doi:10.3233/ICA-170552.
• [42] Peng H, Yang J, Wang J, Wang T, Sun Z, Song X, Luo X, Huang X. Spiking neural P systems with multiple channels, Neural Networks, 2017, 95:66-71. doi:10.1016/j.neunet.2017.08.003.
• [43] Song X, Wang J, Peng H, Ning G, Sun Z, Wang T, Yang F. Spiking neural P systems with multiple channels and anti-spikes, Biosystems, 2018, 169-170:13-19. doi:10.1016/j.biosystems.2018.05.004.
• [44] Song B, Zhang C, Pan L. Tissue-like P systems with evolutional symport/antiport rules, Information Sciences, 2016, 378:177-193. doi:10.1016/j.ins.2016.10.046.
• [45] Vasile CI, Pavel AB, Dumitrache I. Universality of enzymatic numberical P systems, International Journal of Computer Mathematics, 2013, 90(4):869-879. doi:10.1080/00207160.2012.748897.
• [46] Vasile CI, Pavel AB, Dumitrache I, Păun Gh. On the power of enzymatic numerical P systems, Acta Informatica, 2012, 49(6):395-412. doi:10.1007/s00236-012-0166-y.
• [47] Wang J, Peng H, Yu W et al. Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks, Engineering Applications of Artificial Intelligence, 2019, 82:102-109. doi:10.1016/j.engappai.2019.03.014.
• [48] Wang J, Shi P, Peng H. Membrane computing model for IIR filter design, Information Sciences, 2016, 329:164-176. doi:10.1016/j.ins.2015.09.011.
• [49] Wang J, Shi P, Peng H, et al. Weighted fuzzy spiking neural P system, IEEE Transactions on Fuzzy Systems, 2013, 21(2):209-220. doi:10.1109/TFUZZ.2012.2208974.
• [50] Wang X, Zhang G, Neri F, et al. Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots, Integrated Computer-Aided Engineering, 2016, 23(1):15-30. doi:10.3233/ICA-150503.
• [51] Wang T, Zhang G, Zhao J, et al. Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems, IEEE Transactions on Power Systems, 2015, 30(3):1182-1194. doi:10.1109/TPWRS.2014.2347699.
• [52] Xue J, Liu X. Lattice based communication P systems with applications in cluster analysis, Soft Computing, 2014, 18:1425-1440. doi:10.1007/s00500-013-1155-y.
• [53] Zhang Z, Pan L. Numerical P systems with thresholds, International Journal of Computers Communications & Control, 2016, 11(2):292-304. doi:10.15837/ijccc.2016.2.2262.
• [54] Zhang G, Rong H, Neri F, et al. An optimization spiking neural P system for approximately solving combinatorial optimization problems, International Journal of Neural Systems, 2014, 24(5):1440006, 1-16. doi:10.1142/S0129065714400061.
• [55] Zhang Z, Wu T, Pan L. On string languages generated by sequential numerical P systems, Fundamenta Informaticae, 2015, 145(4):485-509. doi:10.3233/FI-2016-1372.
• [56] Zhang Z, Wu T, Păun A, Pan L. Numerical P systems with migrating variables, Theoretical Computer Science, 2016, 641(C):85-108. doi:10.1016/j.tcs.2016.06.004.
• [57] Zhang Z, Wu T, Păun A, Pan L. Universal enzymatic numerical P systems with small number of enzymatic variables, Science China Information Sciences, 2018, 61(9):092-103. doi:10.1007/s11432-017-9103-5.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).