Computational power of dynamic threshold neural P systems for generating string languages

被引：6

作者：

Huang, Yue ^{[1
]}

Yi, Wenmei ^{[1
]}

Peng, Hong ^{[1
]}

Wang, Jun ^{[2
]}

Luo, Xiaohui ^{[1
]}

Yang, Qian ^{[1
]}

机构：

[1] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Sichuan, Peoples R China

[2] Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Sichuan, Peoples R China

来源：

THEORETICAL COMPUTER SCIENCE | 2021年 / 851卷

基金：

中国国家自然科学基金;

关键词：

Membrane computing; Dynamic threshold neural P systems; String language; Language generator; FAULT-DIAGNOSIS; ALGORITHM; RULES;

D O I：

10.1016/j.tcs.2020.10.021

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Inspired from spiking and dynamic mechanisms of neurons, dynamic threshold neural P systems (DTNP systems) have been developed and their computational completeness as number-generating/accepting devices and function computing devices has been investigated. However, a universality result of DTNP systems as language generators has not been established so far. This paper discusses computational power of DTNP systems as language generators. We first discuss the relationship between the languages generated by DTNP systems and finite languages, and then prove that regular languages can be generated by finite DTNP systems. Moreover, we prove that recursively enumerable languages can be characterized by projections of inverse-morphic images of the languages generated by DTNP systems. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：77 / 91

页数：15

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