Complex-valued wavelet network

Authors:
Chunguang Li;Xiaofeng Liao;Juebang Yu
Affiliations:
Lab 570, Institute of Electronic Systems, College of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China;Lab 570, Institute of Electronic Systems, College of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu and Dept. of Comp. Sci. and Eng., Chongqing Universit ...;Lab 570, Institute of Electronic Systems, College of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
Venue:
Journal of Computer and System Sciences
Year:
2003

Citing 5
Cited 2

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Complex-valued radial basis function network, Part I: network architecture and learning algorithms

Signal Processing
Complex-valued radial basis function network, part II: application to digital communications channel equalisation

Signal Processing
Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Complex-valued neural networks with adaptive spline activationfunction for digital-radio-links nonlinear equalization

IEEE Transactions on Signal Processing

Complex-valued wavelet artificial neural network for Doppler signals classifying

Artificial Intelligence in Medicine
Learning algorithm of wavelet network based on sampling theory

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a complex-valued wavelet network (CWN) is proposed. The network has complex inputs, outputs, connection weights, dilation and translation parameters, but the nonlinearity of the hidden nodes remains a real-valued function (real-valued wavelet function). This kind of network is able to approximate an arbitrary nonlinear function in complex multi-dimensional space, and it provides a powerful tool for nonlinear signal processing involving complex signals. A complex algorithm is derived for the training of the proposed CWN. A numerical example on nonlinear communication channel identification is presented for illustration.