Solving NP-Complete Problems by Spiking Neural P Systems with Budding Rules

Ishdorj, Tseren-Onolt; Leporati, Alberto; Pan, Linqiang; Wang, Jun

doi:10.1007/978-3-642-11467-0_24

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…(3) Until now the models used in the fault diagnosis method have static network structures. It can be further investigated whether it is possible to construct fault diagnosis methods using the SNPS models with dynamic network structure, such as SNPS with structural plasticity [14] and SNPS with neuron budding rules [55] or SNPS with neuron division and budding rule [56,57], etc., which can increase the size of the synapse graph. Furthermore, we can compare the performance of these models with the existing models with static network.…”

Section: Remarkmentioning

confidence: 99%

“…These rules of the SNPS systems also play an important role in solving computationally hard problem. The SNPS systems with budding rules [55], SNPS with neuron division and budding rules [56,57] and SNPS with structural plasticity [58] are examples of such models. By applying the budding rules in a maximal parallel manner, in polynomial time an SNPS is capable of increasing the size of its synapse graph.…”

Section: Solving Computationally Hard Problemsmentioning

confidence: 99%

See 1 more Smart Citation

On Applications of Spiking Neural P Systems

Fan¹,

Paul²,

Wu³

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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