A Cluster Splitting Technique by Hopfield Networks and P Systems on Simplices

Liu, Xiyu; Xue, Jie

doi:10.1007/s11063-016-9577-z

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…A P system can be seen as a multicompartmental computing model (with the compartments delimited by membranes) characterized by the following points: [3][4][5][6] • its structure (i.e., membrane structure) which can be a hierarchical arrangement of membranes (thus represented by means of a tree) or as a net of membranes and thus represented by a general graph, • its multisets, i.e., the molecules placed inside each membrane, with their multiplicity, • its evolutionary rules that govern the operations on the multisets or the passage of the molecules across membranes.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Communication on Request

Pan

Păun

Zhang

et al. 2017

Int. J. Neur. Syst.

162

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Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

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Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Communication on Request

Pan

Păun

Zhang

et al. 2017

Int. J. Neur. Syst.

162

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“…The numbers of features (dimensions), data points (instances), and clusters vary in each of the datasets. The details of the synthetic and real-world datasets are listed in Tables 1 and 2, respectively. The performance of KST-DPC was compared with those of the well-known clustering algorithms SC [32], DBSCAN [33], and DPC-KNN [28,34]. The codes for SC and DBSCAN are provided by their authors.…”

Section: Test and Analysismentioning

confidence: 99%

“…Liu et al [27] proposed an improved Apriori algorithm based on an Evolution-Communication tissue-Like P System. Liu and Xue [28] introduced a P system on simplices. Zhao et al [29] proposed a spiking neural P system with neuron division and dissolution.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

Jiang

Liu

Sun

2019

Mathematical Problems in Engineering

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This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

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“…Simultaneously, in [111], the authors studied new techniques for solving a Mitogen Activated Protein Kinases (MAPK) cascade by means of P systems, using P-Lingua and MeCoSim. Other applications emerged in 2016 and 2017 [112][113][114][115] and will continue appearing. In addition, new approaches for the simulation of P systems keep coming up, as it is the case of an interesting agent-based simulator for kernel P systems with division rules [116], from the same team that also developed kPWorkbench [117,162], providing another interesting set of tools for the simulation and verification of kernel P systems.…”

Section: The Era Of Practical Applications Based On P Systemsmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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As with any fast-emerging research front in computer science, the proliferation of theoretical and practical results within Membrane computing since its appearance in 1998 was astonishing. As a consequence, it became necessary during the subsequent years to produce several surveys collecting the main achievements from a theoretical point of view, along with some specific surveys about simulation tools for this paradigm. As the discipline has reached a certain degree of maturity, more practical applications have arisen, and new collective works are summarising the new software products appeared. However, while these recapitulation efforts remain useful for details about new simulators, they cannot act as exhaustive updated listings, as they become obsolete as soon as new tools are developed. Thus, we considered that it was necessary to provide an interactive tool showing an updated timeline (https ://www.gcn.us.es/Simul ation MC) about the simulation of the computational devices of membrane computing (a.k.a P systems), aiming to stay updated whenever any new practical work comes out in the discipline. This paper recalls the main stages and milestones within the evolution of simulation tools for different types and variants of P systems, along with their main related applications. In addition, it describes the interactive web tool with the timeline mentioned, where all the references related here have been incorporated. Unlike other survey papers, it is the intent of this work to reinforce this initial collective effort with the web endpoint kept alive and updated.

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A Cluster Splitting Technique by Hopfield Networks and P Systems on Simplices

Cited by 21 publications

References 32 publications

Spiking Neural P Systems with Communication on Request

Spiking Neural P Systems with Communication on Request

A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

An interactive timeline of simulators in membrane computing

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