Please use this identifier to cite or link to this item:
Title: Wind Shear Forecasting by Chaotic Oscillatory-based Neural Networks (CONN) with Lee Oscillator (Retrograde Signalling) Model
Authors: Wong, MHY
Lee, RST
Liu, JNK
Keywords: Chaos
Neural network
Wind shear
Coupled oscillators
Issue Date: 2008
Publisher: IEEE
Source: IJCNN 2008 : proceedings of the International Joint Conference on Neural Networks : Hong Kong, China, June 1-6, 2008, p. 2040-2047 How to cite?
Abstract: Wind shear is a conventionally unpredictable meteorological phenomenon which presents a common danger to aircraft, particularly on takeoff and landing at airports. This paper describes a method for forecasting wind shear using an advanced paradigm from computational intelligence, chaotic oscillatory-based neural networks (CONN). The method uses weather data to predict wind velocities and directions over a short time period. This approach may have a wide variety of applications but from the aviation forecast perspective, it can be used in aviation to generate wind shear alerts.
ISBN: 978-1-4244-1821-3
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Wing Shear Forecasting_08.pdf642.1 kBAdobe PDFView/Open
View full-text via PolyU eLinks SFX Query
Show full item record


Citations as of Feb 12, 2016


Last Week
Last month
Citations as of Feb 6, 2016

Page view(s)

Checked on Feb 7, 2016

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.