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
Link To Document