HARMONIC DETECTION USING FEED FORWARD ARTIFICIAL NEURAL NETWORKS

被引:0
|
作者
Gunturkun, Rustu [1 ]
Temurtas, Feyzullah [2 ]
Yumusak, Nejat [2 ]
机构
[1] Dumlupinar Univ, Simav Tech Educ Fac, Kutahya, Turkey
[2] Sakarya Univ, Fac Engn, Comp Engn Dept, Sakarya, Turkey
关键词
Feed forward artificial neural networks; active filter; harmonic detection; hidden layer;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, the method to apply the feed forward neural networks with two different numbers of hidden layers for harmonic detection process in active filter are described. We have simulated the distorted wave including 5th, 7th, 11th, 13th harmonics and used them for training of the neural networks. The distorted wave including up to 25th harmonics were prepared for testing of the neural networks. Feed forward neural networks have been used to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively. The results of the neural network with two hidden layers are better than that of the other.
引用
收藏
页码:137 / 143
页数:7
相关论文
共 50 条
  • [21] Building Occupancy Detection Using Feed Forward Back-Propagation Neural Networks
    Das, Sushmita
    Swetapadma, Aleena
    Panigrahi, Chinmoy
    2017 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NETWORKS (CINE), 2017, : 63 - 67
  • [22] Feed-Forward Neural Networks Based on the Eigenstates of the Quantum Harmonic Oscillator
    Rigatos, Gerasimos
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2006, 10 (04) : 567 - 577
  • [23] Estimation of spherical harmonic coefficients in sound field recording using feed-forward neural networks
    Lingkun Zhang
    Xiaochen Wang
    Ruimin Hu
    Dengshi Li
    Weipin Tu
    Multimedia Tools and Applications, 2021, 80 : 6187 - 6202
  • [24] Estimation of spherical harmonic coefficients in sound field recording using feed-forward neural networks
    Zhang, Lingkun
    Wang, Xiaochen
    Hu, Ruimin
    Li, Dengshi
    Tu, Weipin
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (04) : 6187 - 6202
  • [25] Fixed-point implementations for feed-forward artificial neural networks
    Llamocca, Daniel
    INTEGRATION-THE VLSI JOURNAL, 2023, 92 : 1 - 14
  • [26] Solving Differential Equations by Means of Feed-Forward Artificial Neural Networks
    Wojciechowski, Marek
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I, 2012, 7267 : 187 - 195
  • [27] Training Feed-Forward Artificial Neural Networks with a modified artificial bee colony algorithm
    Xu, Feiyi
    Pun, Chi-Man
    Li, Haolun
    Zhang, Yushu
    Song, Yurong
    Gao, Hao
    NEUROCOMPUTING, 2020, 416 : 69 - 84
  • [28] Feed-forward neural networks
    Bebis, George
    Georgiopoulos, Michael
    IEEE Potentials, 1994, 13 (04): : 27 - 31
  • [29] Optimal identification using feed-forward neural networks
    Vergara, V
    Sinne, S
    Moraga, C
    FROM NATURAL TO ARTIFICIAL NEURAL COMPUTATION, 1995, 930 : 1052 - 1059
  • [30] Evapotranspiration estimation using feed-forward neural networks
    Kisi, Ozgur
    NORDIC HYDROLOGY, 2006, 37 (03) : 247 - 260