具有周期激活函数的多值神经元——一种新的学习策略

V. M. Lupea
{"title":"具有周期激活函数的多值神经元——一种新的学习策略","authors":"V. M. Lupea","doi":"10.1109/ICCP.2012.6356164","DOIUrl":null,"url":null,"abstract":"Multi-Value Neurons (MVN) arouse from the necessity to solve those highly non-linear, multi-threshold problems. Various activation functions and learning algorithms were developed in the last years, which in time gave birth to a new type of MVN, called MVN with a periodic activation function (MVN-P). This neuron presented higher functionality than a MVN. The next natural step was trying to integrate this MVN-P into a Neural Network (NN) to increase further more its functionality.","PeriodicalId":406461,"journal":{"name":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-Valued Neuron with a periodic activation function — New learning strategy\",\"authors\":\"V. M. Lupea\",\"doi\":\"10.1109/ICCP.2012.6356164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-Value Neurons (MVN) arouse from the necessity to solve those highly non-linear, multi-threshold problems. Various activation functions and learning algorithms were developed in the last years, which in time gave birth to a new type of MVN, called MVN with a periodic activation function (MVN-P). This neuron presented higher functionality than a MVN. The next natural step was trying to integrate this MVN-P into a Neural Network (NN) to increase further more its functionality.\",\"PeriodicalId\":406461,\"journal\":{\"name\":\"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCP.2012.6356164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCP.2012.6356164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

多值神经元(MVN)是解决高度非线性、多阈值问题的需要而产生的。近年来,各种激活函数和学习算法的发展,催生了一种新型的MVN,即带周期激活函数的MVN- p。这个神经元比MVN表现出更高的功能。下一个自然的步骤是尝试将这个MVN-P集成到神经网络(NN)中,以进一步增加其功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Valued Neuron with a periodic activation function — New learning strategy
Multi-Value Neurons (MVN) arouse from the necessity to solve those highly non-linear, multi-threshold problems. Various activation functions and learning algorithms were developed in the last years, which in time gave birth to a new type of MVN, called MVN with a periodic activation function (MVN-P). This neuron presented higher functionality than a MVN. The next natural step was trying to integrate this MVN-P into a Neural Network (NN) to increase further more its functionality.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信