本文選題：膜計(jì)算 + 脈沖神經(jīng)P系統(tǒng)��； 參考：《西華大學(xué)》2013年碩士論文

【摘要】：膜計(jì)算（又稱P系統(tǒng)）是從生命細(xì)胞的結(jié)構(gòu)與功能以及組織和器官中細(xì)胞群的協(xié)作中抽象出來的計(jì)算模型。P系統(tǒng)是一類分布式、并行性計(jì)算模型。從結(jié)構(gòu)上看，P系統(tǒng)有三種形式：細(xì)胞型P系統(tǒng)、組織型P系統(tǒng)和神經(jīng)型P系統(tǒng)。并行計(jì)算特性是P系統(tǒng)的優(yōu)勢之一，對眾多應(yīng)用問題的求解頗具吸引力的。然而，由于當(dāng)前計(jì)算機(jī)的串行結(jié)構(gòu)原因，P系統(tǒng)的并行計(jì)算還無法真正地模擬或仿真。 GPU（Graphic Processing Unit，圖形處理器）是一個(gè)相對于CPU的概念，最初的設(shè)計(jì)理念是為了協(xié)助CPU處理圖像，它擁有并行處理硬件架構(gòu)和強(qiáng)大的浮點(diǎn)運(yùn)算能力，以實(shí)現(xiàn)圖像處理的硬件加速。如何模擬或仿真各類P系統(tǒng)的并行計(jì)算能力是當(dāng)前膜計(jì)算研究的一個(gè)熱點(diǎn)，因此GPU的出現(xiàn)，特別是其支持矩陣運(yùn)算的并行實(shí)現(xiàn)，，為該研究提供了一個(gè)新的途徑。 本文主要選取兩種脈沖神經(jīng)P系統(tǒng)，實(shí)現(xiàn)其矩陣表示并給出了它們的GPU實(shí)現(xiàn)算法。詳細(xì)的研究工作如下： （1）研究并提出了耗盡型脈沖神經(jīng)P系統(tǒng)并行計(jì)算的矩陣表示。根據(jù)這個(gè)矩陣表示，給出了耗盡型脈沖神經(jīng)P系統(tǒng)的GPU實(shí)現(xiàn)算法。幾個(gè)示例的仿真結(jié)果說明了其GPU實(shí)現(xiàn)的可行性。 （2）針對時(shí)延脈沖神經(jīng)P系統(tǒng)，提出了其并行計(jì)算的矩陣表示，并進(jìn)一步研究了了GPU實(shí)現(xiàn)算法。通過幾個(gè)示例的仿真，驗(yàn)證了延時(shí)脈沖神經(jīng)P系統(tǒng)并行計(jì)算的GPU實(shí)現(xiàn)的可行性和有效性。
[Abstract]:Membrane computing (also called P system) is a computing model abstracted from the structure and function of living cells and the cooperation of cell groups in tissues and organs. P system is a kind of distributed parallel computing model. There are three types of P system: cellular P system, tissue P system and nerve P system. Parallel computing is one of the advantages of P system, and it is attractive to solve many application problems. However, due to the current serial structure of computers, parallel computing in P system can not be really simulated or simulated. GPU (graphic processing Unit) is a concept relative to CPU. The original design idea is to assist CPU in image processing. It has parallel processing hardware architecture and powerful floating-point computing ability to achieve hardware acceleration of image processing. How to simulate or simulate the parallel computing capability of various P systems is a hot topic in the research of membrane computing. Therefore, the appearance of GPU, especially the parallel implementation of matrix operations, provides a new way for the research. In this paper, two kinds of impulsive neural P systems are selected to realize their matrix representation and their GPU implementation algorithms are given. The detailed research work is as follows: (1) the matrix representation of parallel computation for depleted impulsive neural P systems is studied and proposed. According to this matrix representation, a GPU implementation algorithm for depleted impulsive neural P system is presented. The simulation results of several examples show the feasibility of the GPU implementation. (2) for the delay impulsive neural P system, the matrix representation of parallel computation is proposed, and the GPU implementation algorithm is further studied. Through several examples, the feasibility and effectiveness of parallel GPU implementation for delayed impulsive neural P system are verified.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP38;TP391.41

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