A P System for Solving All-Solutions of TSP

Guo, Ping; Xiang, Junqi; Xie, Jingya; Zheng, Jinhang

doi:10.14569/ijacsa.2017.080949

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The models of P system are mainly divided into three types, namely, the cell-like P system [2], the tissue-like P system [3] and the neural-like P system [4]. They have been applied to solve the problems such as NP problems [5]- [8], image processing [9], [10], arithmetic operations [11]- [14] and so on.…”

Section: Introductionmentioning

confidence: 99%

The P System Design Method based on the P Module

Guo¹,

Peng²,

Lian³

2018

ijacsa

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Abstract-Membrane computing is a kind of biocomputing model. At present, the main research areas of membrane computing are computational models and P system design. With the expansion of the P system scale, how to rapidly construct the P system has become a prominent issue. Designing P system based on P module is a P system design method proposed in recent years. This method provides information hiding and can build P system through recursive combination. However, the current P module design lacks a unified design method and lacks the standard process of building P system from P module. This paper studies the structural characteristics of cell-like P systems, and proposes an improved P module design method and a process for assembling P systems through P modules. In order to fully expound the design method of P module, the P system for the square root of the large number was analyzed and designed. And the correctness of the P system based on the P module design method was verified by an instance.

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Section: Introductionmentioning

confidence: 99%

The P System Design Method based on the P Module

Guo¹,

Peng²,

Lian³

2018

ijacsa

Self Cite

View full text Add to dashboard Cite

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An Attribute Reduction P System Based on Rough Set Theory

Guo

Xiang

2018

Communications in Computer and Information Science

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Delayed Spiking Neural P Systems with Scheduled Rules

Ren

Liu

2021

Complexity

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Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.

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A P System for Solving All-Solutions of TSP

Cited by 3 publications

References 14 publications

The P System Design Method based on the P Module

The P System Design Method based on the P Module

An Attribute Reduction P System Based on Rough Set Theory

Delayed Spiking Neural P Systems with Scheduled Rules

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