A proposed self-organizing radial basis function network for aero-engine thrust estimation

Li, Zhiqiang; Zhao, Yong-Ping; Cai, Zhiyuan; Xi, Peng-Peng; Pan, Ying-Ting; Huang, Gong; Zhang, Tianhong

doi:10.1016/j.ast.2019.01.033

Cited by 31 publications

(12 citation statements)

References 25 publications

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“…rough formula (9), we can regard S as a set, which contains a variety of semantic information and context information of the word vector and is consistent with the dimension of the word vector, both of them are N-dimensional vector. erefore, the construction of the sentence vector can be regarded as a process of mapping the sentence to a N-dimensional feature space.…”

Section: Generation and Operation Of Chinese Word Vector Andmentioning

confidence: 99%

“…e network could better predict the total heat transfer coefficient and pressure drop and other heat transfer parameters [8]. Li et al proposed a self-organizing radial basis function neural network algorithm for aeroengine thrust estimation, which can effectively determine the connection weights and generate high-precision self-organizing neural network, proving the effectiveness and practicability of the algorithm in aeroengine thrust estimation [9]. Dey et al has improved the probabilistic neural network classifier based on self-organizing map and the multilayer perceptron classifier based on self-organizing map, compared it with the traditional classifiers, and proved that the algorithm has better classification effect [10].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Chinese Language Feature Analysis Based on Multilayer Self‐Organizing Neural Network and Data Mining Techniques

Liu

Zhang

2021

Computational Intelligence and Neuroscience

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As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

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Section: Generation and Operation Of Chinese Word Vector Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Chinese Language Feature Analysis Based on Multilayer Self‐Organizing Neural Network and Data Mining Techniques

Liu

Zhang

2021

Computational Intelligence and Neuroscience

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“…When the width σ is larger, the graph of the function is wider and the response range of x is correspondingly larger. On the contrary, when the width σ is smaller, the graph of the function is narrower, and the response range of x is correspondingly smaller [ 9 ]. Only samples close enough to the center can activate neurons, so only some neurons in the hidden layer contribute to the output.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function‐Particle Swarm Optimization Neural Network (RBFNN‐PSO)

Chen

2021

Computational Intelligence and Neuroscience

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This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.

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“…An algorithm for building self-organizing Radial Basis Function neural networks for estimating aeroengine thrust is presented by Li et al [22]. The method has the capability to calculate the link weights and optimize the size of the neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

Ullah¹,

Li²,

Khan

et al. 2023

IEEE Access

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A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network's ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.

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A proposed self-organizing radial basis function network for aero-engine thrust estimation

Cited by 31 publications

References 25 publications

[Retracted] Chinese Language Feature Analysis Based on Multilayer Self‐Organizing Neural Network and Data Mining Techniques

[Retracted] Chinese Language Feature Analysis Based on Multilayer Self‐Organizing Neural Network and Data Mining Techniques

[Retracted] Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function‐Particle Swarm Optimization Neural Network (RBFNN‐PSO)

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

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