A Stall Warning Scheme for Aircraft Engines With Inlet Distortion via Deterministic Learning

Lin, Peng; Chen, Tianrui

doi:10.1109/tcst.2017.2709273

Cited by 13 publications

(2 citation statements)

References 23 publications

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“…e artificial neural network algorithm has good performance in the classification and prediction of complex systems, so it is a very meaningful work to apply the artificial neural network algorithm to the research of sports injury risk early warning [11]. e concept of big data network is introduced to establish a new sports injury assessment model based on the big data network.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

He¹

2021

Complexity

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Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

He¹

2021

Complexity

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“…Rapid recognition of test dynamical patterns can be achieved according to the principle of the smallest synchronization error. The deterministic learning approach for identification and recognition of dynamical patterns has been applied to enable modeling and rapid detection of small oscillation faults yielded from several classes of nonlinear dynamical systems [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%