Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Yin, Xiu; Liu, Xiyu; Sun, Minghe; Ren, Qianqian

doi:10.3390/pr9030549

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…Numerical SNP systems (or NSNP systems) [17,53] are SNP system variants which are largely dissimilar to many variants of SNP systems, especially to the variants considered in this paper, for at least two main reasons: (1) rules in NSNP systems do not use regular expressions, and instead use linear functions, so that rules are applied when certain values or threshold of the variables in such functions are satisfied, and (2) the variables in the functions are real-valued, unlike the natural numbers associated with strings and regular expressions. One of the main goals in [17] for introducing NSNP systems is to create an SNP system variant, which in a future work may be more feasible for use with training algorithms in traditional neural networks [53]. For these reasons, we plan to extend our algorithms and compressed data structures for NSNP systems.…”

Section: Discussionmentioning

confidence: 99%

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

et al. 2021

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To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation.

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Section: Discussionmentioning

confidence: 99%

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

et al. 2021

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“…The NSN P system, described in detail below, has a slightly different threshold from that used in the literature [ 30 , 33 ].…”

Section: The Nsn P System and Its Extension To The Frnsn P Systemmentioning

confidence: 99%

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Yin

Liu

Sun

et al. 2022

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The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

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“…Under the continuous research of a wide range of scholars, several different variants of SNP systems have been proposed. Numerical SNP (NSNP) systems [5][6][7] are a variant of SNP systems inspired by numerical P systems, in which information is encoded by the values of variables and processed by continuous functions. Compared to the original SNP systems, NSNP systems are no longer discrete, but have a continuous numerical nature, which is useful for solving real-world problems.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Rules Dynamic Generation and Removal

Shen

Zhao

2023

Applied Sciences

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Spiking neural P systems (SNP systems), as computational models abstracted by the biological nervous system, have been a major research topic in biological computing. In conventional SNP systems, the rules in a neuron remain unchanged during the computation. In the biological nervous system, however, the biochemical reactions in a neuron are also influenced by factors such as the substances contained in it. Based on this motivation, this paper proposes SNP systems with rules dynamic generation and removal (RDGRSNP systems). In RDGRSNP systems, the application of rules leads to changes of the substances in neurons, which leads to changes of the rules in neurons. The Turing universality of RDGRSNP systems is demonstrated as a number-generating device and a number-accepting device, respectively. Finally, a small universal RDGRSNP system for function computation using 68 neurons is given. It is demonstrated that the variant we proposed requires fewer neurons by comparing it with five variants of SNP systems.

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Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Cited by 8 publications

References 42 publications

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Spiking Neural P Systems with Rules Dynamic Generation and Removal

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