Using LIDAR doppler velocity data and chaotic oscillatory-based neural network for the forecast of meso-scale wind field

Kwong, K. M.; Liu, J.N.K.; Chan, Pak Wai; Lee, R.

doi:10.1109/cec.2008.4631064

Cited by 21 publications

(15 citation statements)

References 6 publications

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“…Chaotic behavior provides a rich library of behaviors to aid computer systems, such as weather forecasting (Kwong et al 2008;Wong et al 2008;Glushkov et al 2009), communications (Lawrance and Ohama 2003), and robot control (Arsenio 2004) or laser control (Karim 2009). Neural networks mimic the flexible nature of biological systems and offer a wide range of potential applications.…”

Section: A Wind Shear Predictionmentioning

confidence: 98%

An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting

Kwong

Wong

Liu

et al. 2012

Journal of Atmospheric and Oceanic Technology

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Current research based on various approaches including the use of numerical weather prediction models, statistical models, and machine learning models have provided some encouraging results in the area of longterm weather forecasting. But at the level of mesoscale and even microscale severe weather phenomena (involving very short-term chaotic perturbations) such as turbulence and wind shear phenomena, these approaches have not been so successful. This research focuses on the use of chaotic oscillatory-based neural networks for the study of a mesoscale weather phenomenon, namely, wind shear, a challenging and complex meteorological problem that has a vital impact on aviation safety. Using lidar data collected at the Hong Kong International Airport via the Hong Kong Observatory, it is possible to forecast the Doppler velocities with satisfactory accuracy and validate the prediction model with the potential to generate the wind shear alert. Experimental results are found to be comparable to the actual measurement. Moreover, the selected testing cases and results show that the value of correlation coefficient between the predicted and lidar-measured wind velocities exceeds 0.9 with various window sizes ranging from 1 to 3 h. These provide areas for further research of the proposed model and lidar technology for turbulence and wind shear forecasts.

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Section: A Wind Shear Predictionmentioning

confidence: 98%

An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting

Kwong

Wong

Liu

et al. 2012

Journal of Atmospheric and Oceanic Technology

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“…This theory established the basis of many subsequent studies on optimization problems and models in cognitive information processing (see, e.g., [22]- [26]) including synchronization and desynchronization behaviors of neural oscillators. Recent applications include pattern and memory association, scene analysis, and pattern recognition ( [8], [27]- [29]). As chaotic natures exhibit in the atmosphere, chaotic oscillators combined with ANNs have produced CONN that copies human neural behaviors to address weather forecasting problems.…”

Section: F Chaotic Oscillatormentioning

confidence: 99%

“…The number of hidden layers (set to 1) and hidden neurons (7)(8) were chosen experimentally since there is no simple clear-cut method for determining these parameters [7]. Characters A and B in the neurons indicate different parameter settings used with the chaotic oscillatory model.…”

Section: Training and Testing Of The Chaotic Oscillatory-based Neumentioning

confidence: 99%

Chaotic Oscillatory-Based Neural Network for Wind Shear and Turbulence Forecast With LiDAR Data

Liu

Kwong

Chan³

2012

IEEE Trans. Syst., Man, Cybern. C

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This paper focuses on the forecast of wind shear and turbulence at the Hong Kong International Airport. It presents a mesoscale prediction model that uses chaotic oscillatory-based neural networks (CONN) to forecast the evolution of wind fields along the glide path in the vicinity of the airport. This model makes use of accurate Doppler velocities measured by light detection and ranging (LiDAR) system and afterward collected by the Hong Kong Observatory. Simulation results show that the CONN model with a new learning algorithm is able to capture the occurrence, evolution, and sudden changes of the winds representing turbulence incidences in the region. Research findings show that Doppler velocities forecast using CONN can be transformed into headwind profiles and processed with the developed algorithm to identify the wind shear occurrence. These are shown to match actual observations made using LiDAR in terms of time, locations, and size of wind shear events with considerable accuracy. The model has better performance compared with that of the traditional multilayered perceptron model neural network. The results encourage further exploration and experimentation in the use of machine learning and chaotic neural network in weather forecast and alerting.Index Terms-Chaotic oscillator, chaotic oscillatory-based neural networks (CONN) model, neural network, turbulence, wind shear.

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“…In [14], a chaotic neural network using logistic chaotic nodes and nonlinear dynamic recurrent associative memory architecture is designed to enhance the performance of the usual memory. Furthermore, chaotic based neural networks find an application in real forecasting problems [15,16].…”

Section: Introductionmentioning

confidence: 99%

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

Mahdavi

Menhaj

2011

Int. J. Control Autom. Syst.

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In this paper, we consider a Hopfield like Chaotic Neural Networks which have both selfcoupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron's outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron's outputs with each other is guaranteed, when all neuron's have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.

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Using LIDAR doppler velocity data and chaotic oscillatory-based neural network for the forecast of meso-scale wind field

Cited by 21 publications

References 6 publications

An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting

An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting

Chaotic Oscillatory-Based Neural Network for Wind Shear and Turbulence Forecast With LiDAR Data

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

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