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Wang, T., Zhang G., Rong H., & Pérez-Jiménez M. J. (2014).  Application of fuzzy reasoning spiking neural P systems to fault diagnosis. International Journal of Computers, Communications and Control. 9(6), 720-733.
Wang, J., Hoogeboom H. J., Pan L., Paun G., & Pérez-Jiménez M. J. (2010).  Spiking Neural P Systems with Weights. Neural Computation. 22(10), 2615-2646. Abstract
Wang, T., Zhang G., Pérez-Jiménez M. J., & Cheng J. (2015).  Weighted Fuzzy Reasoning Spiking Neural P Systems: Application to Fault Diagnosis in Traction Power Supply Systems of High-Speed Railways. Journal of Computational and Theoretical Nanoscience. 12(7), 1003-1114. Abstract
Wang, T., Wei X., Huang T., Wang J., Peng H., Pérez-Jiménez M. J., et al. (2019).  Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration. IEEE Access. 7, 12798-12808.
Wang, J., Hu J., Peng H., Pérez-Jiménez M. J., & Riscos-Núñez A. (2015).  Decision tree models induced by membrane systems. Romanian Journal of Information Science and Technology. 18(3), 228–239.
Wang, J., Peng H., Yu W., Ming J., Pérez-Jiménez M. J., Tao C., et al. (2019).  Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks. Engineering Applications of Artificial Intelligence. 82, 102 - 109. Abstract
Wang, J., Shi P., Peng H., Pérez-Jiménez M. J., & Wang T. (2013).  Weighted Fuzzy Spiking Neural P Systems. IEEE Transactions on Fuzzy Systems. 2, 209-220. Abstract
Wang, J., Peng H., Tu M., Pérez-Jiménez M. J., & Shi P. (2016).  A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms. Signal Processing. 25, 320-327. Abstract
Wang, T., Wei X., Wang J., Huang T., Peng H., Song X., et al. (2020).  A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies. Engineering Applications of Artificial Intelligence. 92, 103680. Abstract
Woods, D., Murphy N., Pérez-Jiménez M. J., & Riscos-Núñez A. (2009).  Membrane dissolution and division in P. Lecture Notes in Computer Science. 5717, 262-276. Abstract
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Xian, R., Xiong X., Peng H., Wang J., Ramírez-de-Arellano-Marrero A., & Yang Q. (2024).  Feature fusion method based on spiking neural convolutional network for edge detection. Pattern Recognition. 147, 110112. Abstract
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Yang, J., Chen R., Zhang G., Peng H., Wang J., & Riscos-Núñez A. (2018).  A Kernel-Based Membrane Clustering Algorithm. Enjoying Natural Computing: Essays Dedicated to Mario de Jesús Pérez-Jiménez on the Occasion of His 70th Birthday. 318-329.
Ye, L., Zheng J., Guo P., & Pérez-Jiménez M. J. (2019).  Solving the 0-1 Knapsack Problem by Using Tissue P System With Cell Division. IEEE Access. 7, 66055-66067.
Yuan, W., Zhang G., Pérez-Jiménez M. J., Wang T., & Huan Z. (2015).  P systems based computing polynomials: Design and formal verification. Asian Conference on Membrane Computing (ACMC 2015). 1, 1-9. Abstract
Yuan, W., Zhang G., Pérez-Jiménez M. J., Wang T., & Huang Z. (2016).  P systems based computing polynomials: design and formal verification. Natural Computing. 15(4), 591–596.
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Zandron, C., Leporati A., Ferretti C., Mauri G., & Pérez-Jiménez M. J. (2008).  On the computational efficiency of polarizationless recognizer P systems with strong division and dissolution. 6th Brainstorming Week on Membrane Computing. 6, 261-274. Abstract
Zandron, C., Leporati A., Ferretti C., Mauri G., & Pérez-Jiménez M. J. (2008).  On the computational efficiency of polarizationless recognizer P systems with strong division and dissolution. Fundamenta Informaticae. 87(1), 79-91. Abstract
Zang, T., Lei J., Wei X., Huang T., Wang T., Pérez-Jiménez M. J., et al. (2019).  Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches. IEEE Access. 7, 88927-88936. Abstract
Zeng, X. X., Adorna H., Martínez-del-Amor M. A., & Pan L. (2010).  When Matrices Meet Brains. Eighth Brainstorming Week on Membrane Computing. 255-266.
Zeng, X. X., Adorna H., Martínez-del-Amor M. A., Pan L., & Pérez-Jiménez M. J. (2010).  Matrix representation of Spiking Neural P Systems. (Marian Gheorghe, Thomas Hinze, Gheorghe Paun, Ed.).Eleventh International Conference on Membrane Computing (CMC11). 425-439. Abstract
Zeng, X. X., Adorna H., Martínez-del-Amor M. A., Pan L., & Pérez-Jiménez M. J. (2011).  Matrix representation of Spiking Neural P systems. Lecture Notes in Computer Science. 6501, 377-392. Abstract
Zeng, X. X., Pan L., & Pérez-Jiménez M. J. (2014).  Small universal simple Spiking Neural P Systems with weights. Science China. Information Sciences. 57(9), 1-11. Abstract
Zhang, G., Rong H., Ou Z., Pérez-Jiménez M. J., & Gheorghe M. (2014).  Automatic Design of Deterministic and Non-Halting Membrane Systems by Tuning Syntactical Ingredients. IEEE Transactions on NanoBioscience.
Zhang, G., Gutiérrez-Naranjo M. A., Qin Y., & Gheorghe M. (2012).  A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Reconfiguration. Tenth Brainstorming Week on Membrane Computing. II, 277-292. Abstract
Zhang, G., Shang Z., Verlan S., Martínez-del-Amor M. Á., Yuan C., Valencia-Cabrera L., et al. (2020).  An Overview of Hardware Implementation of Membrane Computing Models. ACM Computing Surveys. 53(4), 38.