Double Layers Self-Organized Spiking Neural P Systems With Anti-Spikes for Fingerprint Recognition

Ma, Tongmao; Hao, Shaohua; Wang, Xun; Rodríguez‐Patón, Alfonso; Wang, Shudong; Song, Tao

doi:10.1109/access.2019.2958895

Cited by 13 publications

(11 citation statements)

References 36 publications

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“…Fingerprint recognition is a challenging problem in pattern recognition. In 2017, Ma et al introduced a framework based on SNPS to solve this problem [34]. More specifically, a double-layer self-organized SNPS with anti-spikes model was introduced to perform this task and the working of this model different from the working of other models mentioned in this section.…”

Section: Pattern Recognitionmentioning

confidence: 99%

“…Many variants of SNPS have been introduced by incorporating features from the biological neurons such as asynchronous systems [8], astrocytes [9], rule on synapses [10], communication on request [11,12], synapses with schedules [13], structural plasticity [14], weighted synapses [15], inhibitory synapses [16], anti-spikes [17], etc. These models have also been used in solving problems related to real-life applications, such as fault diagnosis of power systems [18][19][20][21][22][23][24][25][26][27][28][29][30][31], pattern recognition [32][33][34], computational biology [35], performing arithmetic and logical operations and hardware implementation [36][37][38][39][40][41][42][43][44][45][46][47], solving computational hard problems [18,[48][49][50][51][52]…”

Section: Introductionmentioning

confidence: 99%

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On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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Section: Pattern Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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“…These characteristics mean the SNP systems have good application prospects in solving many practical problems. At present, some scholars have proven the feasibility of SNP systems to solve pattern recognition problems [21][22][23][24][25], combined with algorithms to solve optimization problems [26][27][28], clustering [29], automatic design [30], fault diagnosis [31][32][33][34], and perform arithmetic and logic operations [35][36][37][38], implemented by software and hardware [39,40].…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Membrane Computing Models

Liu

Ren

2021

Processes

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As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.

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“…Much theoretical work has been done on SN P systems, e.g., their normal forms [14][15][16], formal representations [17][18][19], and their relations to classical models of computation [20][21][22][23][24][25] with a short and recent survey in [26]. After much theoretical work, more recently the work to apply SN P systems to real-world problems becomes even more active, with some early works on image processing e.g., [27] and more recently in [28], use for cryptography [29][30][31], use of evolutionary algorithms to design SN P systems [32][33][34], in pattern recognition [35,36], computational biology [37], with a recent survey in [38].…”

Section: Introductionmentioning

confidence: 99%

Snapse: A Visual Tool for Spiking Neural P Systems

et al. 2020

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Spiking neural P (SN P) systems are models of computation inspired by spiking neurons and part of the third generation of neuron models. SN P systems are equivalent to Turing machines and are able to solve computationally hard problems using a space-time trade-off. Research in SN P systems theory is especially active, more so in recent years as more efforts are directed towards their real-world applications. Usually, SN P systems are represented visually as a directed graph and simulated through mainly text-based simulations or tables. Thus, there is a need for tools that can simulate and create SN P Systems in a visual and easy-to-use manner. Snapse is such a tool which aims to hasten the speed and ease at which researchers may create and experiment with SN P systems. Furthermore, visual tools such as Snapse can help further bring SN P systems outside of theoretical computer science.

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Double Layers Self-Organized Spiking Neural P Systems With Anti-Spikes for Fingerprint Recognition

Cited by 13 publications

References 36 publications

On Applications of Spiking Neural P Systems

On Applications of Spiking Neural P Systems

Spiking Neural Membrane Computing Models

Snapse: A Visual Tool for Spiking Neural P Systems

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