Numerical P Systems with Thresholds

Zhang, Zhiqiang; Pan, Linqiang

doi:10.15837/ijccc.2016.2.2262

Cited by 22 publications

(4 citation statements)

References 15 publications

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“…Recently, several variants of numerical P systems have been proposed, such as numerical P systems with thresholds [36], numerical P systems with migrating variables [37]. It has been proved that both the strategy of using thresholds and the feature of migrating variables can improve the computational power of numerical P systems.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Universal enzymatic numerical P systems with small number of enzymatic variables

Zhang

Păun

et al. 2017

Sci. China Inf. Sci.

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Numerical P systems (for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems (for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the systems. Yet the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach universality. These results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Universal enzymatic numerical P systems with small number of enzymatic variables

Zhang

Păun

et al. 2017

Sci. China Inf. Sci.

Self Cite

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“…By introducing enzymatic variables, Pavel et al [34] proposed enzymatic numerical P systems, where whether a program is executed depends on the value of the corresponding enzymatic variable. Zhang and Pan [35] investigated numerical P systems with thresholds. Similar to the enzymatic variable, the threshold was regarded as a control mechanism: when each variable in a production function is higher (resp., lower) than a threshold, the program can be executed.…”

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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“…NPS is a new special research branch of cell-like P systems, proposed by the founder of MC, Gh.Păun in 2006 [45]. In NPS, multisets of objects associated to membranes are sets of numerical variables, and the evolutionary rules are composed of a production function and a repartition protocol [46,47,48]. The most common widely application area of NPS is robot controller design [49,50,51,52].…”

Section: MC and Enpsmentioning

confidence: 99%

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

et al. 2019

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Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

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Numerical P Systems with Thresholds

Cited by 22 publications

References 15 publications

Universal enzymatic numerical P systems with small number of enzymatic variables

Universal enzymatic numerical P systems with small number of enzymatic variables

Stochastic Numerical P Systems With Application in Data Clustering Problems

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

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