区间2型模糊神经网络的混合学习算法

2007 IEEE International Conference on Granular Computing (GRC 2007) Pub Date : 2007-11-02 DOI:10.1109/GrC.2007.116

J. R. Castro, O. Castillo, P. Melin, Antonio Rodríguez Díaz

{"title":"区间2型模糊神经网络的混合学习算法","authors":"J. R. Castro, O. Castillo, P. Melin, Antonio Rodríguez Díaz","doi":"10.1109/GrC.2007.116","DOIUrl":null,"url":null,"abstract":"In this paper, a class of interval type-2 fuzzy neural networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for a fuzzy-neural system is as follows: it starts with the development of an \"interval type-2 fuzzy neuron\", which is based on biological neural morphologies, followed by learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks\",\"authors\":\"J. R. Castro, O. Castillo, P. Melin, Antonio Rodríguez Díaz\",\"doi\":\"10.1109/GrC.2007.116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a class of interval type-2 fuzzy neural networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for a fuzzy-neural system is as follows: it starts with the development of an \\\"interval type-2 fuzzy neuron\\\", which is based on biological neural morphologies, followed by learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture.\",\"PeriodicalId\":259430,\"journal\":{\"name\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GrC.2007.116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Granular Computing (GRC 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GrC.2007.116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 23

摘要

本文提出了一类与区间2型模糊推理系统功能等价的区间2型模糊神经网络(IT2FNN)。设想的模糊神经系统的计算过程如下:首先是基于生物神经形态的“区间2型模糊神经元”的发展，然后是学习机制。我们描述了如何分解参数集，使自适应网络的混合学习规则可以应用于IT2FNN体系结构。

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

Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks

In this paper, a class of interval type-2 fuzzy neural networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for a fuzzy-neural system is as follows: it starts with the development of an "interval type-2 fuzzy neuron", which is based on biological neural morphologies, followed by learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture.

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

2007 IEEE International Conference on Granular Computing (GRC 2007)

自引率

0.00%

发文量