Moving targets pattern recognition based on the wavelet neural network

Ge, Guangying; Chen, Lili; Juan, Xu

doi:10.1007/bf02687991

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…However, there is currently no explicit quantification formula for the number of nodes in the hidden layer. Rough estimates can be made based on empirical formulas [50,51].…”

Section: Establishment Of Neural Networkmentioning

confidence: 99%

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Wei,

Zhu,

Cao

et al. 2024

JMSE

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A moored spar platform, equipped with various instruments, serves as a crucial tool in hydrological monitoring. However, conducting dynamic analyses of a single spar that endures wind and current requires significant amount of computational time. To address this challenge, this study proposes an efficient surrogate model to represent fluid loads. A database is established to capture the relationship between fluid loads, spar displacements and uniform currents based on a numerical model of the spar. Subsequently, an artificial neural network method is employed to construct the surrogate model. Finally, the surrogate model is integrated with a numerical model of the cable, developed using the lumped mass method, to create a coupled model of the moored spar. The dynamic responses of this coupled model align closely with those obtained from the purely numerical model, demonstrating the efficacy of the surrogate model in capturing fluid loads on the spar. In addition to the surrogate model generation approach, this research provides an efficient method to couple the surrogate model with the numerical model in dynamic analysis of floating systems in uniform currents.

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“…However, there is currently no explicit quantification formula for the number of nodes in the hidden layer. Rough estimates can be made based on empirical formulas [50,51].…”

Section: Establishment Of Neural Networkmentioning

confidence: 99%

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Wei,

Zhu,

Cao

et al. 2024

JMSE

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show abstract

“…Due to a three layer feed-forward neural network can approach any complex functions, so choose a three layer BP network model [5], shows in Fig. 1.…”

mentioning

confidence: 99%

Fault Diagnosis of Gearbox based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm

Duan¹,

Yuan²,

Xiong³

2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

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Abstract. In order to improve accuracy of fault diagnosis of gearbox, adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize weight of BP. According to fault feature, fault diagnosis is accomplished by optimized BP. The algorithm overcomes disadvantages that slowly convergence and easy to fall into local minima of standard PSO and BP. The simulation results show that the method gains good classification result and has a certain practicality.

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Moving targets pattern recognition based on the wavelet neural network

Cited by 2 publications

References 4 publications

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Fault Diagnosis of Gearbox based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm

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