Title :
Complex domain backpropagation
Author :
Georgiou, George M. ; Koutsougeras, Cris
Author_Institution :
Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA
fDate :
5/1/1992 12:00:00 AM
Abstract :
The backpropagation algorithm is extended to complex domain backpropagation (CDBP) which can be used to train neural networks for which the inputs, weights, activation functions, and outputs are complex-valued. Previous derivations of CDBP were necessarily admitting activation functions that have singularities, which is highly undesirable. In the derivation, CDBP is derived so that that it accommodates classes of suitable activation functions. One such function is found and the circuit implementation of the corresponding neuron is given. CDBP hardware circuits can be used to process sinusoidal signals all at the same frequency (phasors)
Keywords :
analogue computer circuits; computerised signal processing; learning systems; neural nets; activation functions; complex domain backpropagation; hardware circuits; neural network training; phasors; sinusoidal signals processing; Backpropagation algorithms; Circuits; Feedforward systems; Frequency; Least squares approximation; Neural network hardware; Neural networks; Neurons; Nonhomogeneous media; Signal processing;
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
