发布求助
文献互助 智能选刊 最新文献

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks最新文献

筛选
英文 中文
Group Models of Artificial Polynomial and Trigonometric Higher Order Neural Networks 人工多项式和三角高阶神经网络的群模型
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-3563-9.ch003
{"title":"Group Models of Artificial Polynomial and Trigonometric Higher Order Neural Networks","authors":"","doi":"10.4018/978-1-7998-3563-9.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-3563-9.ch003","url":null,"abstract":"Real-world financial data is often discontinuous and non-smooth. Neural network group models can perform this function with more accuracy. Both polynomial higher order neural network group (PHONNG) and trigonometric polynomial higher order neural network group (THONNG) models are studied in this chapter. These PHONNG and THONNG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover, they are capable of handling higher frequency, higher order nonlinear, and discontinuous data. Results confirm that PHONNG and THONNG group models converge without difficulty and are considerably more accurate (0.7542% - 1.0715%) than neural network models such as using polynomial higher order neural network (PHONN) and trigonometric polynomial higher order neural network (THONN) models.","PeriodicalId":236860,"journal":{"name":"Emerging Capabilities and Applications of Artificial Higher Order Neural Networks","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data Classification Using Ultra-High Frequency SINC and Trigonometric Higher Order Neural Networks 基于超高频SINC和三角高阶神经网络的数据分类
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-3563-9.ch007
{"title":"Data Classification Using Ultra-High Frequency SINC and Trigonometric Higher Order Neural Networks","authors":"","doi":"10.4018/978-1-7998-3563-9.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-3563-9.ch007","url":null,"abstract":"This chapter develops a new nonlinear model, ultra high frequency sinc and trigonometric higher order neural networks (UNT-HONN), for data classification. UNT-HONN includes ultra high frequency sinc and sine higher order neural networks (UNS-HONN) and ultra high frequency sinc and cosine higher order neural networks (UNC-HONN). Data classification using UNS-HONN and UNC-HONN models are tested. Results show that UNS-HONN and UNC-HONN models are more accurate than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UNS-HONN and UNC-HONN models can classify data with error approaching 10-6.","PeriodicalId":236860,"journal":{"name":"Emerging Capabilities and Applications of Artificial Higher Order Neural Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125029483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页
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
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
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：604180095
Book学术官方微信