A Turing machine simulation by P systems without charges

Leporati, Alberto; Manzoni, Luca; Mauri, Giancarlo; Porreca, Antonio E.; Zandron, Claudio

doi:10.1007/s41965-020-00031-5

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…Membrane computing (MC) (Pan et al, 2019;Leporati et al, 2020;Zhang, 2021), a new branch of natural computing, has developed into an important research direction in the past decades. MC (the models are called P systems), initiated by Prof. Pǎun (Paun, 2000), abstracts computing models inspired from the structure and the functioning of the biological cells, organs and colonies of bacteria.…”

Section: Introductionmentioning

confidence: 99%

A method of single-phase grounding fault line selection based on optimization spiking neural P systems

Tian¹,

Jiang²,

Li³

et al. 2022

Front. Energy Res.

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It is important to select the fault line rapidly when single-phase grounding fault occurs in the small current grounding system. The fault information acquisition of existing methods generally need hardware modification, so the high cost makes it difficult to apply to the power grid in underdeveloped areas. Taking that into consideration, this paper proposed a method of steady-state information small current grounding fault line selection based on Optimization Spiking Neural P Systems (OSNPS). The method only needs the steady-state voltage and current data of the dispatch side to effectively identify the fault line, which greatly improves the range of application. According to the characteristics of power dispatching big data, the objective function is established and the normalized model parameters are optimized by OSNPS to improve the accuracy of fault line selection stably. Furthermore, PSCAD/EMTDC is used to simulate the small current grounding system, the main factors affecting the accuracy of fault line selection are analyzed and the relationship between fault information features and fault identification accuracy is revealed. What’s more, It is pointed out that the model parameters without optimization may have line selection failure. Finally, specific examples are given to verify that the model parameters optimized by OSNPS can effectively improve the accuracy of fault line selection.

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Section: Introductionmentioning

confidence: 99%

A method of single-phase grounding fault line selection based on optimization spiking neural P systems

Tian¹,

Jiang²,

Li³

et al. 2022

Front. Energy Res.

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show abstract

“…In [8,11], solutions for the SAT problem are proposed which use different strategies than previously proposed solutions. Systems of a shallow depth are the subject of [20][21][22]. A recent survey on different strategies to approach computationally hard problems by P systems with active membranes can be found in [39].…”

Section: Introductionmentioning

confidence: 99%

Evaluating space measures in P systems

et al. 2022

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P systems with active membranes are a variant of P systems where membranes can be created by division of existing membranes, thus creating an exponential amount of resources in a polynomial number of steps. Time and space complexity classes for active membrane systems have been introduced, to characterize classes of problems that can be solved by different membrane systems making use of different resources. In particular, space complexity classes introduced initially considered a hypothetical real implementation by means of biochemical materials, assuming that every single object or membrane requires some constant physical space (corresponding to unary notation). A different approach considered implementation of P systems in silico, allowing to store the multiplicity of each object in each membrane using binary numbers. In both cases, the elements contributing to the definition of the space required by a system (namely, the total number of membranes, the total number of objects, the types of different membranes, and the types of different objects) was considered as a whole. In this paper, we consider a different definition for space complexity classes in the framework of P systems, where each of the previous elements is considered independently. We review the principal results related to the solution of different computationally hard problems presented in the literature, highlighting the requirement of every single resource in each solution. A discussion concerning possible alternative solutions requiring different resources is presented.

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“…In [7,10] solutions for SAT are proposed which use different strategies than previously proposed solutions. Systems of a shallow depth are the subject of [17][18][19]. A recent survey on different strategies to approach computationally hard problems by P systems with active membranes can be found in [36].…”

Section: Introductionmentioning

confidence: 99%

Alternative space definitions for P systems with active membranes

et al. 2021

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The first definition of space complexity for P systems was based on a hypothetical real implementation by means of biochemical materials, and thus it assumes that every single object or membrane requires some constant physical space. This is equivalent to using a unary encoding to represent multiplicities for each object and membrane. A different approach can also be considered, having in mind an implementation of P systems in silico; in this case, the multiplicity of each object in each membrane can be stored using binary numbers, thus reducing the amount of needed space. In this paper, we give a formal definition for this alternative space complexity measure, we define the corresponding complexity classes and we compare such classes both with standard space complexity classes and with complexity classes defined in the framework of P systems considering the original definition of space.

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A Turing machine simulation by P systems without charges

Cited by 15 publications

References 17 publications

A method of single-phase grounding fault line selection based on optimization spiking neural P systems

A method of single-phase grounding fault line selection based on optimization spiking neural P systems

Evaluating space measures in P systems

Alternative space definitions for P systems with active membranes

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