基于粗糙规则的脉冲神经膜系统计算能力的研究  

Computing Ability of Spiking Neural P System Based on Rough Rules

作　　者：罗云芳[1] 唐承娥[1] 韦军 LUO Yun-fang;TANG Cheng-e;WEI Jun(College of Electromechanical and Information Engineering,Guangxi Vocational and Technical College,Nanning 530226,China;Guangxi Zhuang Autonomous Region Admission Examination Institute,Nanning 530021,China)

机构地区：[1]广西职业技术学院机电与信息工程学院,南宁530226 [2]广西壮族自治区招生考试院,南宁530021

出　　处：《计算机科学》2020年第S01期626-630,642,共6页Computer Science

基　　金：广西职业技术学院2018年科研课题(181102);2018年广西高校中青年教师基础能力提升项目(2018KY0951,2018KY0956)。

摘　　要：脉冲神经膜系统是受到神经生物系统中神经元相互协作处理脉冲过程的启发而提出的一种新的计算模型。为了更进一步反映生物系统随机性的特点,文中首先提出一种新脉冲神经系统——粗糙规则脉冲神经膜系统,用上下近似概念来确立神经元的激活条件;然后证明了改进后脉冲神经膜系统的计算完备性;最后研究系统产生自动化语言能力来说明其具有很强的计算能力。Spiking neural P system inspired neurons cooperation processing pulse process in biological systems and proposed new calculation model.In order to further reflect the randomness of biological system,this paper proposed a new neuronal activation system,rough rule based spiking neural P system,which uses the concept of upper and lower approximations to establish the activation conditions of neurons.Then,the computing completeness of the improved spiking neural P system was proved.Finally,the ability of the system to generate automatic language was studied to illustrate its computing ability.The result shows that the improved spiking neural P system has strong computing ability.

关 键 词：脉冲神经 粗糙规则 自动化语言 计算能力 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]