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 :
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