Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1781
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
Forecasting
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.
URI: http://hdl.handle.net/10397/1781
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.
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