Trajectory P system

Annadurai, S.; Kalyani, T.; Dare, V. R.; Thomas, D. G.

doi:10.1016/j.pnsc.2008.01.003

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…In [15], trajectory is drawn into P system for the first time, and a so called trajectory P system is proposed. In a trajectory P system, shuffle operation deals with words from the same language set in each region, and this operation is applied in a special kind of sequential way.…”

Section: Introductionmentioning

confidence: 99%

P systems with shuffle operation

Niu

Subramaniant

2010

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

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Shuffle operation on trajectories is useful in modeling parallel composition of words and languages. In this work, a new class of P systems with shuffle operation is presented. Such a system has language-objects and shuffle operation rules in its regions. It can be used as a language generator; specifically it is used to generate languages from sets of languages. Some comparison results are obtained, which show that the power of shuffle operation is enlarged in the framework of P systems.

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Section: Introductionmentioning

confidence: 99%

P systems with shuffle operation

Niu

Subramaniant

2010

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

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“…Another important P system is the tissue P system which is topologically a graph [25]. There are many tissue P systems such as rule applicability based communication modes [4], and trajectory P systems with evolutionary rules [3]. Spiking neural P systems are extensively studied recently by Pan and his team [17].…”

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confidence: 99%

A Cluster Splitting Technique by Hopfield Networks and P Systems on Simplices

Liu

Xue

2017

Neural Process Lett

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In this paper we propose a new graph based P system. The main feature of graph P system is that membrane compartments spread on edges as well as on vertices. These two kinds of compartments are topologically connected with certain communication rules. A new neural computing technique is proposed to solve cluster splitting into finding the equilibrium of Hopfield neural networks. Then we use the new graph P systems to obtain stable status of the neural networks. An example is presented to show the computing efficiency of the new P system with comparison with classical genetic algorithms.

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