非线性脉冲神经P系统作为函数计算设备的最小通用性研究  

The Minimum Versatility of Nonlinear Spiking Neural P Systems as a Function Computing Device

作　　者：杨倩 YANG Qian(School of Computer and Software Engineering,Xihua University,Chengdu Sichuan 610039,China)

机构地区：[1]西华大学计算机与软件工程学院,四川成都610039

出　　处：《信息与电脑》2022年第14期53-55,共3页Information & Computer

摘　　要：膜计算(P系统)的理论研究是为了得到等价于图灵机的高性能的非传统计算模型。在各类计算模型中,对于计算资源一般是需要的资源越少越好。在脉冲神经P(Spiking Neural P,SNP)系统中,计算资源通过神经元的个数来衡量。非线性脉冲神经膜(Nonlinear Spiking Neural P,NSNP)系统是SNP系统的一个变体,目前已经证明有一个用于计算函数的NSNP系统共有117个神经元。笔者利用注册机的特性,将该系统神经元减少至63个。The theoretical research of membrane computing(P system) is to obtain a high-performance and non-traditional computing model which is equivalent to turing machine. Generally, in all kinds of computing models, the less computing resources are needed, the better. In the Spiking Neural P(SNP) system, computing resources are measured by the number of neurons. Nonlinear Spiking Neural P(NSNP)system is a variant of SNP system. It has been proved that there is an NSNP system for calculating functions, with a total of 117 neurons. In this paper, the number of neurons in the system is reduced to 63 by using the characteristics of the registration machine.

关 键 词：膜计算 脉冲神经P(SNP)系统 非线性 注册机 通用性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]