概率膜系统在大熊猫种群动态性建模中的应用

发布时间：2018-06-20 22:49

本文选题：膜计算 + 概率膜系统　；参考：《西南交通大学》2017年硕士论文

【摘要】：在计算机科学中,膜计算是一个非常前沿的研究领域。膜计算的计算模型(称为P系统或膜系统)是受生物细胞的启发而探索出来的。膜系统具有离散性、极大并行性、模块化和分布式、可扩展性和非确定性等诸多适合应用研究和理论研究的显著特点。随着膜计算的不断发展,研究者提出概率膜系统,并将其应用于研究实际生态系统的种群动态性。概率膜系统应用于生态系统种群动态性建模,不仅能充分展示膜计算的显著特点,而且为生态系统种群动态性研究提供了新的方法,是膜计算应用研究中正在开展的新兴方向。大熊猫是非常珍贵的动物,对其进行种群动态性研究,有利于管理者更好的掌握大熊猫种群数量变化趋势。因此,本文使用概率膜系统研究分析大熊猫种群动态性。论文的主要工作及研究成果如下:1.构建圈养大熊猫种群动态性概率膜计算模型。首先,详细地介绍使用概率膜系统对生态系统种群动态性建模的一般方法,然后,基于成都大熊猫繁育研究基地大熊猫谱系数据,设计了一个具有两层膜结构,形式化表示大熊猫生态系统的一系列对象和规则的概率膜计算模型,最后使用MeCoSim软件仿真实现构建的模型。2.构建圈养大熊猫种群动态性概率膜计算扩展模型。在上一章构建的概率膜计算模型中加入救护模块,设计了研究大熊猫种群动态性的概率膜计算扩展模型,并使用MeCoSim软件对构建的扩展模型进行仿真实现。3.构建圈养大熊猫种群动态性概率膜计算改进模型。对上一章构建的概率膜计算扩展模型中繁殖模块进行优化,使其繁殖规则更加符合大熊猫实际繁殖行为,设计了研究大熊猫种群动态性的概率膜计算改进模型,并使用MeCoSim软件仿真实现构建的改进模型。本文研究工作是国家自然科学基金项目(大熊猫生态系统膜计算模型建模机制研究,批准号:61672437)的主要研究内容。
[Abstract]:Membrane computing is a very frontier research field in computer science. The computational model of membrane computing (called P system or membrane system) was explored by biological cells. Membrane systems are characterized by discreteness, maximum parallelism, modularization and distribution, extensibility and uncertainty, etc. With the development of membrane computing, the probabilistic membrane system is proposed and applied to study the population dynamics of real ecosystem. The application of probabilistic membrane system to ecosystem population dynamic modeling can not only fully demonstrate the remarkable characteristics of membrane computing, but also provide a new method for the study of ecosystem population dynamics. It is a new direction in the research of membrane computing application. Giant panda is a very precious animal. It is helpful for managers to study the population dynamics of giant panda. Therefore, the dynamics of giant panda population is studied by using probabilistic membrane system. The main work and research results are as follows: 1. The dynamic probability membrane model of captive giant panda population was established. Firstly, the general method of using probabilistic membrane system to model the dynamics of ecosystem population is introduced in detail. Then, based on the data of giant panda pedigree in Chengdu Giant Panda breeding Research Base, a two-layer membrane structure is designed. The probabilistic membrane computing model for describing a series of objects and rules of giant panda ecosystem is formalized. Finally, the model constructed by MeCoSim software is implemented. The dynamic probability membrane model of captive giant panda population was established. In the last chapter, the probabilistic membrane computing expansion model is designed to study the dynamics of giant panda population by adding rescue module into the probability membrane computing model, and the extended model is simulated by MeCoSim software. An improved model for dynamic probability membrane calculation of captive giant panda population was established. This paper optimizes the propagation module in the expanded model of probability membrane computing, which is constructed in the previous chapter, and makes its reproduction rules more consistent with the actual reproduction behavior of giant panda. An improved model of probability membrane calculation for studying the dynamics of giant panda population is designed. The improved model is realized by using MeCoSim software simulation. The research work of this paper is the main research content of the project of National Natural Science Foundation of China (Research on the Modeling Mechanism of Giant Panda ecosystem membrane Computation Model, Grant No.: 6167243737).
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：Q958.1

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