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spiking neural p systems with neuron division and budding

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Science China(Information Sciences) 2011年第8期54卷 1596-1607页

作者： p■UN Gheorghe pREZ-JIMNEZ Mario J. Department of Computer Science and Artificial Intelligence University of Sevilla Avda Reina Mercedes s/n 41012 Sevilla Spain Institute of Mathematics of the Romanian Academy pO Box 1-764 014700 Bucuresti Romania

spiking neural p systems are a class of distributed and parallel computing models inspired by spiking *** this work,the features of neuron division and neuron budding are introduced into the framework of spiking neural p systems,which are processes inspired by neural stem cell division. With neuron division and neuron budding,a spiking neural p system can generate exponential work space in polynomial time as the case for p systems with active *** this way,spiking neural p systems can efficiently solve computationally hard problems by means of a space-time tradeoff,which is illustrated with an efficient solution to SAT problem.

关键词： natural computing membrane computing spiking neural p system neuron division

Reversible spiking neural p systems

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Frontiers of Computer Science 2013年第3期7卷 350-358页

作者： Tao SONG Xiaolong SHI Jinbang XU Key Laboratory of Image processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

spiking neural （SN） p systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN p systems, as well as the computing power of reversible SN p systems. Reversible SN p systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.

关键词： membrane computing spiking neural p system reversible computing model universality reversible register machine

A fault segment location method for distribution networks based on spiking neural p systems and Bayesian estimation

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protection and Control of Modern power systems 2023年第3期8卷 184-195页

作者： Yi Wang Tao Wang Liyuan Liu School of Electrical Engineering and Electronic Information Xihua UniversityChengdu 610039China Key Laboratory of Fluid and power Machinery Ministry of EducationXihua UniversityChengdu 610039China

With the increasing scale of distribution networks and the mass access of distributed generation,traditional central-ized fault location methods can no longer meet the performance requirements of speed and high ***-fore,this paper proposes a fault segment location method based on spiking neural p systems and Bayesian estimation for distribution networks with distributed ***,the distribution network system topology is decoupled into single-branch networks.A spiking neural p system with excitatory and inhibitory synapses is then proposed to model the suspected faulty segment,and its matrix reasoning algorithm is executed to obtain a preliminary set of location ***,the Bayesian estimation and contradiction principle are applied to verify and correct the ini-tial results to obtain the final location *** results based on the IEEE 33-node system validate the feasi-bility and effectiveness of the proposed method.

关键词： Distribution network Fault location spiking neural p system Bayesian estimation Contradiction principle

A Low-carbon Operation Optimization Method of ETG-RIES Based on Adaptive Optimization spiking neural p systems

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protection and Control of Modern power systems 2024年第5期9卷 162-177页

作者： Tao Wang Zhu Huang Ruixuan Ying Luis Valencia-Cabrera School of Electrical Engineering and Electronic Information Xihua UniversityChengdu 610039China the Xiluodu Hydropower plant China Yangtze Power Co.Ltd.Zhaotong 657300China the Institute of Multidisciplnary Research for Ad-vanced Materials Tohoku UniversitySendai 980-8577Japan Department of Computer Science and Artificial Intelligence University of SevilleAvda.Reina Mercedes s/n 41012Spain

To enhance multi-energy complementarity and foster a low carbon economy of energy resources,this paper proposes an innovative low-carbon operation opti-mization method for electric-thermal-gas regional inte-grated energy *** bolster the low-carbon operation capabilities of such systems,a coordinated operation framework is presented that integrates carbon capture devices,power to gas equipment,combined heat and power units,and a multi-energy storage *** address the challenge of high-dimensional constraint imbalance in the optimization process,a novel low-carbon operation opti-mization method is then *** new method is based on an adaptive single-objective continuous optimiza-tion spiking neural p system,specifically designed for this ***,simulation models of four typical schemes are established and employed to test and analyze the economy and carbon environmental pollution degree of the proposed system model,as well as the performance of the operation optimization ***,simulation results show that the proposed method not only considers the economic viability of the target integrated energy sys-tem,but also significantly improves the wind power utilization and carbon reduction capabilities.

关键词： spiking neural p system power-to-gas membrane computing regional integrated energy system low-carbon operation optimization

Small universal simple spiking neural p systems with weights

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Science China(Information Sciences) 2014年第9期57卷 19-29页

作者： ZENG XiangXiang pAN LinQiang pREZ-JIMNEZ Mario J. Key Laboratory of Image processing and Intelligent Control School of AutomationHuazhong University of Science and Technology Department of Computer Science and Artificial Intelligence University of SevillaAvda. Reina Mercedes s/nSevilla 41012Spain

spiking neural p systems with weights(WSN p systems,for short)are a new variant of spiking neural p systems,where the rules of a neuron are enabled when the potential of that neuron equals a given *** is known that WSN p systems are universal by simulating register ***,in these universal systems,no bound is considered on the number of neurons and *** this work,a restricted variant of WSN p systems is considered,called simple WSN p systems,where each neuron has only one *** complexity parameter,the number of neurons,to construct a universal simple WSN p system is *** is proved that there is a universal simple WSN p system with 48 neurons for computing functions;as generator of sets of numbers,there is an almost simple(that is,each neuron has only one rule except that one neuron has two rules)and universal WSN p system with 45 neurons.

关键词： bio-inspired computing membrane computing p system spiking neural p system universal computing device

power Transformer Fault Diagnosis Using Fuzzy Reasoning spiking neural p systems

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Journal of Intelligent Learning systems and Applications 2016年第4期8卷 77-91页

作者： Yousif Yahya Ai Qian Adel Yahya Department of Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Industrial Engineering Al-Zawia University Regdalen Libya

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning spiking neural p systems (FRSN p systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN p systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

关键词： Dissolved Gas Analysis Fault Diagnosis Fuzzy Reasoning power Transformer Faults spiking neural p system