Numerical P systems with Boolean condition

Liu, Liucheng; Yi, Wenmei; Yang, Qian; Peng, Hong; Wang, Jun

doi:10.1016/j.tcs.2019.03.021

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…Pan et al [36] investigated numerical P systems with production thresholds, where threshold control mechanism is reflected on the function value of production function. Liu et al [37] presented a more general control mechanism, i.e., Boolean condition. Usually, the variables are located at different regions, and their positions are given initially and remain unchanged during the computation.…”

Section: Remarkmentioning

confidence: 99%

Stochastic Numerical P Systems With Application in Data Clustering Problems

et al. 2020

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This paper proposes an extension of numerical P (NP) systems, called stochastic numerical P (StNP) systems. In StNP systems, a novel stochastic production function-repartition protocol is developed to evolve the variables. Compared with usual production function-repartition protocol in NP systems, there are two differences in stochastic production function-repartition protocol: (i) some stochastic/randomized factors are introduced in production function and/or repartition protocol; (ii) data updating way is relaxed and can be flexibly used. An StNP system has a nested structure and contains some variables and a group of programs. The programs consist of stochastic production function-repartition protocols and/or usual production function-repartition protocols. Therefore, StNP systems are a class of distributed parallel computing models with stochastic and dynamic characteristics. Data clustering problems are used as an application to demonstrate the availability and effectiveness of StNP systems. Based on StNP systems, a novel partition clustering algorithm is presented. Experimental results demonstrate advantage of StNP systems for data clustering problems.

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Section: Remarkmentioning

confidence: 99%

Stochastic Numerical P Systems With Application in Data Clustering Problems

et al. 2020

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“…The distributed parallel computing models obtained from the development of membrane computing are membrane systems, also known as P systems, and the study of P systems has been divided into three types according to the cell membrane structure or cell distribution: cell-like P systems, tissue-like P systems, and neural-like P systems. For the theoretical research of membrane computing, three types of P systems have been extended to obtain several universal computational models [ 3 , 4 , 5 , 6 , 7 ], and the computational complexity of extended P systems has been explored [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. For the application research of membrane computing, existing studies have realized the integration of membrane computing with algorithms for applications in robot control [ 21 , 22 ], data modeling, and optimization [ 23 , 24 , 25 , 26 ], algorithms for solving NP problems [ 27 , 28 , 29 ], clustering algorithms [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], and image processing [ 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes

Liu

Zhao

2022

Entropy

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Spiking neural P systems (SN P systems for short) realize the high abstraction and simulation of the working mechanism of the human brain, and adopts spikes for information encoding and processing, which are regarded as one of the third-generation neural network models. In the nervous system, the conduction of excitation depends on the presence of membrane potential (also known as the transmembrane potential difference), and the conduction of excitation on neurons is the conduction of action potentials. On the basis of the SN P systems with polarizations, in which the neuron-associated polarization is the trigger condition of the rule, the concept of neuronal membrane potential is introduced into systems. The obtained variant of the SN P system features charge accumulation and computation within neurons in quantity, as well as transmission between neurons. In addition, there are inhibitory synapses between neurons that inhibit excitatory transmission, and as such, synapses cause postsynaptic neurons to generate inhibitory postsynaptic potentials. Therefore, to make the model better fit the biological facts, inhibitory rules and anti-spikes are also adopted to obtain the spiking neural P systems with membrane potentials, inhibitory rules, and anti-spikes (referred to as the MPAIRSN P systems). The Turing universality of the MPAIRSN P systems as number generating and accepting devices is demonstrated. On the basis of the above working mechanism of the system, a small universal MPAIRSN P system with 95 neurons for computing functions is designed. The comparisons with other SN P models conclude that fewer neurons are required by the MPAIRSN P systems to realize universality.

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“…The variables evolve through programs consisting of production functions and repartition protocols, which is fundamentally different from most P systems that use multiset rewriting rules to evolve. In order to more effectively control the application of the programs, many variants of NP systems, such as enzymatic NP systems [27], NP systems with production thresholds [28], and NP systems with Boolean conditions [29] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

et al. 2021

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A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.

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Numerical P systems with Boolean condition

Cited by 12 publications

References 36 publications

Stochastic Numerical P Systems With Application in Data Clustering Problems

Stochastic Numerical P Systems With Application in Data Clustering Problems

Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes

Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

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