.png){kind=icon}
.png){kind=icon}
Return
Split quaternion nonlinear adaptive filtering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2009.10.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Quaternion-valued adaptive filters
Nonlinear adaptive filtering
Cauchy-Riemann-Fueter equation
Quaternion Multilayer Perceptron
Wind modelling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.