• DocumentCode
    1799973
  • Title

    Feedforward multilayer phase-based neural networks

  • Author

    Pavaloiu, Ionel-Bujorel ; Vasile, Adrian ; Rosu, Sebastian Marius ; Dragoi, George

  • Author_Institution
    Dept. of Eng. in Foreign Languages, Univ. Politeh. of Bucharest, Bucharest, Romania
  • fYear
    2014
  • fDate
    25-27 Nov. 2014
  • Firstpage
    125
  • Lastpage
    130
  • Abstract
    Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.
  • Keywords
    backpropagation; feedforward neural nets; ANN; CPBN; CVNN; MLPBN; artificial neural networks; backpropagation algorithm; complex-valued inputs; complex-valued neural networks; complex-valued parameters; continuous phase-based neuron; feedforward multilayer phase-based neural networks; Backpropagation algorithms; Biological neural networks; Feedforward neural networks; Neurons; Nonhomogeneous media; Training; Vectors; Backpropagation; Complex-Valued Neural Networks; Phase-Based Neuron;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
  • Conference_Location
    Belgrade
  • Print_ISBN
    978-1-4799-5887-0
  • Type

    conf

  • DOI
    10.1109/NEUREL.2014.7011478
  • Filename
    7011478