具有ω形激活函数的前馈神经网络的性能

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91 Pub Date : 1991-11-10 DOI:10.1109/TAI.1991.167096

Yiwei Chen, F. Bastani

{"title":"具有ω形激活函数的前馈神经网络的性能","authors":"Yiwei Chen, F. Bastani","doi":"10.1109/TAI.1991.167096","DOIUrl":null,"url":null,"abstract":"The capability of the multilayer feedforward layered neural network with Omega -shaped activation functions is studied. The authors prove that a three layer neural network with any continuous Omega -shaped activation function can approximate any continuous function in the multidimensional real space. Further theoretical extensions to the generalized Omega -shaped function are also explored. One example of this kind of neural network is the Hermite network.<<ETX>>","PeriodicalId":371778,"journal":{"name":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The capability of feedforward neural networks with Omega -shaped activation functions\",\"authors\":\"Yiwei Chen, F. Bastani\",\"doi\":\"10.1109/TAI.1991.167096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The capability of the multilayer feedforward layered neural network with Omega -shaped activation functions is studied. The authors prove that a three layer neural network with any continuous Omega -shaped activation function can approximate any continuous function in the multidimensional real space. Further theoretical extensions to the generalized Omega -shaped function are also explored. One example of this kind of neural network is the Hermite network.<<ETX>>\",\"PeriodicalId\":371778,\"journal\":{\"name\":\"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91\",\"volume\":\"104 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1991.167096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1991.167096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

研究了具有ω形激活函数的多层前馈分层神经网络的性能。证明了具有任意连续欧米茄形激活函数的三层神经网络可以逼近多维实空间中的任意连续函数。对广义欧米茄形函数的进一步理论扩展也进行了探讨。这种神经网络的一个例子是赫米特网络。

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

The capability of feedforward neural networks with Omega -shaped activation functions

The capability of the multilayer feedforward layered neural network with Omega -shaped activation functions is studied. The authors prove that a three layer neural network with any continuous Omega -shaped activation function can approximate any continuous function in the multidimensional real space. Further theoretical extensions to the generalized Omega -shaped function are also explored. One example of this kind of neural network is the Hermite network.<>

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

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91

自引率

0.00%

发文量