基于模糊脉冲神经P系统的知识表示

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI:10.1109/BICTA.2010.5645191

Tao Wang, Jun Wang, Hong Peng, Yanli Deng

{"title":"基于模糊脉冲神经P系统的知识表示","authors":"Tao Wang, Jun Wang, Hong Peng, Yanli Deng","doi":"10.1109/BICTA.2010.5645191","DOIUrl":null,"url":null,"abstract":"This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Knowledge representation using fuzzy spiking neural P system\",\"authors\":\"Tao Wang, Jun Wang, Hong Peng, Yanli Deng\",\"doi\":\"10.1109/BICTA.2010.5645191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.\",\"PeriodicalId\":302619,\"journal\":{\"name\":\"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)\",\"volume\":\"113 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BICTA.2010.5645191\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BICTA.2010.5645191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

摘要

本文提出了一个模糊尖峰神经P系统(FSN P系统)来表示基于规则系统知识库中的模糊产生规则，其中模糊产生规则的确定性因子和命题的真值用梯形模糊数来描述。在该系统中，对传统神经元的定义进行了扩展。神经元分为命题神经元和规则神经元两类;每个神经元的内容是[0,1]中的梯形模糊数，而不是整数。此外，该模糊推理过程也可以用FSN - P系统建模。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Knowledge representation using fuzzy spiking neural P system

This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

自引率

0.00%

发文量