@article {2485,
	title = {Nonlinear Spiking Neural P Systems},
	journal = {International Journal of Neural Systems},
	volume = {30},
	year = {2020},
	pages = {2050008},
	doi = {10.1142/S0129065720500082},
	author = {Hong Peng and Zeqiong Lv and Bo Li and Xiaohui Luo and Jun Wang and Xiaoxiao Song and Tao Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Agust{\'\i}n Riscos-N{\'u}{\~n}ez}
}
@article {PENG2020105064,
	title = {Spiking neural P systems with inhibitory rules},
	journal = {Knowledge-Based Systems},
	volume = {188},
	year = {2020},
	pages = {105064},
	abstract = {Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNP-IR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons.},
	keywords = {Inhibitory synapse, Membrane computing, spiking neural P systems, Spiking neural P systems with inhibitory rules},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2019.105064},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705119304514},
	author = {Hong Peng and Bo Li and Jun Wang and Xiaoxiao Song and Tao Wang and Luis Valencia-Cabrera and Ignacio P{\'e}rez-Hurtado and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {WANG2020103680,
	title = {A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {92},
	year = {2020},
	pages = {103680},
	abstract = {This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrix-based reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause{\textendash}Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.},
	keywords = {Cause{\textendash}effect network, Fault diagnosis, Fuzzy reasoning, Membrane computing, power system, Spiking neural P system},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2020.103680},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197620301172},
	author = {Tao Wang and Xiaoguang Wei and Jun Wang and Tao Huang and Hong Peng and Xiaoxiao Song and Luis Valencia Cabrera and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {article,
	title = {Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber-Physical Systems in Smart Grids},
	journal = {Complexity},
	volume = {2019},
	year = {2019},
	month = {03},
	pages = {1-15},
	doi = {10.1155/2019/7428458},
	author = {Tao Wang and Xiaoguang Wei and Tao Huang and Jun Wang and Luis Valencia-Cabrera and Zhennan Fan and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {PENG2019105064,
	title = {Spiking neural P systems with inhibitory rules},
	journal = {Knowledge-Based Systems},
	year = {2019},
	pages = {105064},
	abstract = {Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNP-IR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons.},
	keywords = {Inhibitory synapse, Membrane computing, spiking neural P systems, Spiking neural P systems with inhibitory rules},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2019.105064},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705119304514},
	author = {Hong Peng and Bo Li and Jun Wang and Xiaoxiao Song and Tao Wang and Luis Valencia-Cabrera and Ignacio P{\'e}rez-Hurtado and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {2172,
	title = {Fault diagnosis for multi-energy flows of energy internet: Framework and prospect},
	journal = {2017 IEEE Conference on Energy Internet and Energy System Integration (EI2)},
	year = {2017},
	month = {11/2017},
	pages = {1-5},
	publisher = {IEEE, Inc.},
	isbn = {978-1-5386-1428-0},
	doi = {10.1109/EI2.2017.8245494},
	author = {Tao Wang and Jun Wang and Zhennan Fan and Xiaoguang Wei and Tianlei Zang and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Huang}
}
@article {2111,
	title = {P systems based computing polynomials: design and formal verification},
	journal = {Natural Computing},
	volume = {15},
	year = {2016},
	pages = {591{\textendash}596},
	doi = {https://doi.org/10.1007/s11047-016-9577-y},
	url = {https://link.springer.com/article/10.1007\%2Fs11047-016-9577-y},
	author = {Weitao Yuan and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Wang and Zhiwei Huang}
}
@article {1921,
	title = {P systems based computing polynomials: Design and formal verification},
	journal = {Asian Conference on Membrane Computing (ACMC 2015)},
	volume = {1},
	year = {2015},
	month = {11/2015},
	pages = {1-9},
	abstract = {Automatic design of P systems is an attractive research topic
in the community of membrane computing. Differing from the previous
work that used evolutionary algorithms to fulfill the task, this paper
presents the design of a simple (deterministic transition) P system
(without input membrane) of degree 1, capturing the value of the korder
(k >= 2) polynomial by using a reasoning method. Specifically, the
values of polynomial p(n) corresponding to a natural number t is equal
to the multiplicity of a distinguished object of the system (the output
object) in the configuration at instant t. We also discuss the descriptive
computational resources required by the designed k-order polynomial P
system},
	keywords = {automatic design, Membrane computing, P system, polynomial},
	url = {http://www.cs.us.es/~marper/investigacion/P\%20system\%20solving\%20polynomial\%20problem.pdf},
	author = {Weitao Yuan and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Wang and Zhiwei Huan}
}
@article {1920,
	title = {Temporal Fuzzy Reasoning Spiking Neural P Systems with Real Numbers for Power System Fault Diagnosis},
	journal = {Proceedings of the Asian Conference on Membrane Computing (ACMC 2015)},
	volume = {8},
	year = {2015},
	month = {11/2015},
	pages = {1-20},
	url = {http://www.cs.us.es/~marper/investigacion/rTFRSNP.pdf},
	author = {Kang Huang and Tao Wang and Yangyang He and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1919,
	title = {Fault Section Estimation of Power Systems with Optimization Spiking Neural P Systems},
	journal = {Workshop on Membrane Computing 2015 (WMC2015), Satellite workshop of UCNC2015},
	year = {2015},
	month = {08/2015},
	pages = {117-127},
	publisher = {Centre for Discrete Mathematics and Theoretical Computer Science},
	address = {Auckland, New Zealand},
	url = {http://www.cs.us.es/~marper/investigacion/Proceedings-WMC2015.pdf},
	author = {Tao Wang and Sikui Zeng and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez and Jun Wang}
}
@article {1910,
	title = {Weighted Fuzzy Reasoning Spiking Neural P Systems: Application to Fault Diagnosis in  Traction Power Supply Systems of High-Speed Railways},
	journal = {Journal of Computational and Theoretical Nanoscience},
	volume = {12},
	year = {2015},
	month = {07/2015},
	pages = {1003-1114},
	publisher = {American Scientific Publishers},
	address = {Valencia, CA, USA},
	abstract = {This paper discusses the application of weighted fuzzy reasoning spiking neural P systems (WFRSN P systems) to fault diagnosis in traction power supply systems (TPSSs) of China high-speed railways. Four types of neurons are considered in WFRSN P systems to make them suitable for expressing status information of protective relays and circuit breakers, and a weighted matrix-based reasoning algorithm (WMBRA) is introduced to fulfill the reasoning based on the status information to obtain fault confidence levels of faulty sections. Fault diagnosis production rules in TPSSs and their WFRSN P system models are proposed to show how to use WFRSN P systems to describe different kinds of fault information. Building processes of fault diagnosis models for sections and fault region identification of feeding sections, and parameter setting of the models are described in detail. Case studies including normal power supply and over zone feeding show the effectiveness of the presented method.},
	keywords = {AUTOTRANSFORMER FEEDING; FAULT DIAGNOSIS; HIGH-SPEED RAILWAY; TRACTION POWER SUPPLY SYSTEM; WEIGHTED FUZZY REASONING SPIKING NEURAL P SYSTEM},
	issn = {1546-1955},
	doi = {10.1166/jctn.2015.3857},
	url = {http://www.ingentaconnect.com/content/asp/jctn/2015/00000012/00000007/art00002?token=004e1c2e6e76bf6220bc437a63736a6f5e4763213b763c2553747b6f642f46426b3d6567c345de},
	author = {Tao Wang and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez and Jixiang Cheng}
}
@article {1776,
	title = {Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems},
	journal = {IEEE Transactions on Power Systems},
	volume = {30},
	year = {2015},
	month = {04/2015},
	pages = {1182-1194},
	publisher = {IEEE Press.},
	address = {Athens, Greece},
	abstract = {This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.

Published in:},
	issn = {0885-8950 },
	doi = {10.1109/TPWRS.2014.2347699},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6887379},
	author = {Tao Wang and Gexiang Zhang and Junbo Zhao and Zhengyou He and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1928,
	title = {Fuzzy Membrane Computing: Theory and Applications},
	journal = {International Journal of Computers, Communications and Control},
	volume = {10},
	year = {2015},
	pages = {904-935},
	publisher = {Agora University Editing House - CCC Publications},
	address = {Oradea, Romania},
	abstract = {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.},
	keywords = {fuzzy membrane computing, fuzzy reasoning spiking neural P systems, fuzzy set, linguistic term, Membrane computing, multi-fuzzy set, trapezoidal fuzzy number},
	issn = {1841-9836},
	doi = {doi: 10.15837/ijccc.2015.6.2080},
	url = {http://univagora.ro/jour/index.php/ijccc/article/view/2080},
	author = {Tao Wang and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1775,
	title = {Application of fuzzy reasoning spiking neural P systems to fault diagnosis},
	journal = {International Journal of Computers, Communications and Control},
	volume = {9},
	year = {2014},
	pages = {720-733},
	publisher = {Agora University Editing House - CCC Publications},
	address = {Oradea, Romania},
	issn = {1841-9836},
	author = {Tao Wang and Gexiang Zhang and Haina Rong and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1787,
	title = {Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways},
	journal = {Twelfth Brainstorming Week on Membrane Computing (BWMC2014)},
	year = {2014},
	pages = {329-350},
	publisher = {F{\'e}nix Editora},
	isbn = {978-84-940056-4-0},
	url = {http://www.gcn.us.es/12bwmc_proceedings},
	author = {Tao Wang and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1792,
	title = {Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems},
	journal = {15th Conference on Membrane Computing, pre-proceedings},
	year = {2014},
	pages = {361-374},
	address = {Opava, Czech Republic},
	isbn = {978-80-7510-036-8},
	url = {http://cmc15.slu.cz/wp-content/uploads/2014/01/cmc15_proceedings.pdf},
	author = {Tao Wang and Gexiang Zhang and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {882,
	title = {Weighted Fuzzy Spiking Neural P Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {2},
	year = {2013},
	month = {07/2013},
	pages = {209-220},
	publisher = {IEEE Computational Intelligence Society},
	edition = {21},
	abstract = {Spiking neural P systems (SN P systems) are a new class of computing models inspired by neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable to represent and process fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper, called weighted fuzzy spiking neural P systems (WFSN P systems). Some new elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule and two types of neurons, are added to original definition of SN P systems, which allow WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm based on WFSN P systems is developed, which can accomplish dynamic fuzzy reasoning of a rule-based systems more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph and Petri nets, to demonstrate the features or advantages of the proposed techniques.},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2012.2208974},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6242397},
	author = {Jun Wang and Peng Shi and Hong Peng and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Wang}
}
@article {879,
	title = {Fuzzy reasoning spiking neural P system for fault diagnosis},
	journal = {Information Sciences},
	volume = {235},
	year = {2013},
	month = {06/2013},
	pages = {106{\textendash}116},
	publisher = {Elsevier},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron{\textquoteright}s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.},
	keywords = {Fault diagnosis; P systems; Spiking neural P systems; Fuzzy knowledge representation; Fuzzy reasoning},
	issn = {0020-0255},
	doi = {http://dx.doi.org/10.1016/j.ins.2012.07.015},
	url = {(http://dx.doi.org/10.1016/j.ins.2012.07.015)},
	author = {Hong Peng and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Hao Wang and Jie Shao and Tao Wang}
}
