Fourier series neural networks for regression

Yung Ming Wang, Li Jeng Huang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

An innovative efficient and fast neural networks in which hidden neurons are constructed based on Fourier series expansions (FSNN), half-range cosine (FCSNN) and sine expansions (FSSNN) are proposed and tested for linear and nonlinear regulation problems. The results of numerical examples using FSNN are compared with those obtained from traditional linear regression (LP), nonlinear regression (NLP), backward propagation neural networks (BPANN) and radial basis function neural networks (RBFNN). The results obtained from FSNN agree well with those obtained from LP, NLP, BPANN and RBFNN and show global approximation features to the fitting data. Only a few hidden neurons are required to obtain very good and fast convergence of regression as compared with BPANN and RBFNN.

原文English
主出版物標題Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
編輯Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面716-719
頁數4
ISBN(電子)9781538643426
DOIs
出版狀態Published - 2018 六月 22
事件4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan
持續時間: 2018 四月 132018 四月 17

出版系列

名字Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Other

Other4th IEEE International Conference on Applied System Innovation, ICASI 2018
國家Japan
城市Chiba
期間18-04-1318-04-17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Modelling and Simulation
  • Biomedical Engineering

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