Identification of nonlinear time‐varying systems using wavelet neural networks

Emami, Seyyed Ali; Roudbari, Alireza

doi:10.1002/adc2.59

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…For example, Luo et al developed an identification of autonomous nonlinear dynamical system based on discrete-time multiscale wavelet neural network [16]. Emami developed an identification of nonlinear timevarying systems using wavelet neural networks [34]. However, wavelet neural networks involve adjusting multiple parameters, including the selection of wavelet functions, determination of wavelet scales, and the number of layers and nodes in the neural network.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Zheng,

Liu,

et al. 2023

Mathematics

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Wavelet neural networks have been widely applied to dynamical system identification fields. The most difficult issue lies in selecting the optimal control parameters (the wavelet base type and corresponding resolution level) of the network structure. This paper utilizes the advantages of Legendre multiwavelet (LW) bases to construct a Legendre multiwavelet neural network (LWNN), whose simple structure consists of an input layer, hidden layer, and output layer. It is noted that the activation functions in the hidden layer are adopted as LW bases. This selection if based on the its rich properties of LW bases, such as piecewise polynomials, orthogonality, various regularities, and more. These properties contribute to making LWNNs more effective in approximating the complex characteristics exhibited by uncertainties, step, nonlinear, and ramp in the dynamical systems compared to traditional wavelet neural networks. Then, the number of selection LW bases and the corresponding resolution level are effectively optimized by the simple Genetic Algorithm, and the improved gradient descent algorithm is implemented to learn the weight coefficients of LWNN. Finally, four nonlinear dynamical system identification problems are applied to validate the efficiency and feasibility of the proposed LWNN-GA method. The experiment results indicate that the LWNN-GA method achieves better identification accuracy with a simpler network structure than other existing methods.

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Section: Introductionmentioning

confidence: 99%

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Zheng,

Liu,

et al. 2023

Mathematics

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Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

Wang

Yue

et al. 2021

International Journal of Aerospace Engineering

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Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.

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Identification of nonlinear time‐varying systems using wavelet neural networks

Cited by 2 publications

References 47 publications

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

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