A modeling method for aero-engine by combining stochastic gradient descent with support vector regression

Ren, Li-Hua; Ye, Zhifeng; Zhao, Yong-Ping

doi:10.1016/j.ast.2020.105775

Cited by 47 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…46 As for SVR-SMO, it has been recently used in aero-engine aerodynamic models. 56 An SVR-PUK was used to predict daily cloud free sea surface temperature from a sensor 57 and to spectra. 48 Finally, an SVR-SMO-PUK has been used to predict the wrinkle-based age, 49 and for snow depth retrieval.…”

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

View full text Add to dashboard Cite

The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

show abstract

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…The overall classification accuracy of all algorithms was 87% in the diabetes dataset, 95% in the healthcare stroke prediction dataset, and 82% in the adult dataset. Ren et al [33] proposed a data-driven modeling approach for aeroengine aerodynamic models combining stochastic gradient descent with support vector regression. The modeling method was validated using simulation data from an aero-engine component-level model and flight data from a type of aircraft, and it provided better performance compared with the traditional method.…”

Section: Introductionmentioning

confidence: 99%

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Yuan¹,

Liu²,

Song³

et al. 2023

Phyton

View full text Add to dashboard Cite

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

show abstract

“…Asgari et al 19 applied NARX neural network to simulate the start-up procedure of a single-shaft gas turbine and the findings demonstrate that NARX neural network models are capable of satisfactory prediction of the start-up behavior. Ren et al 20 proposed a novel online Support Vector Regression (SVR) by introducing Stochastic Gradient Descent (SGD) as the nonlinear function of NARX models, and the online SVR was applied to online modeling with the online flight data of a two-rotor turbojet engine.…”

Section: Introductionmentioning

confidence: 99%

Aero-engine on-board model based on big Quick Access Recorder data

Ren

Zhu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

View full text Add to dashboard Cite

With the development of the Full Authority Digital Engine Controller (FADEC) technology, the aero-engine on-board model is widely used in Engine Health Management (EHM) and control. Due to the FADEC’s limited computational capability and storage capacity, the model should not be very intricate; consequently, the interpolation model is widely utilized. Although the interpolation model’s low precision precludes further development of on-board models for EHM and control. To address the trade-off between precision and complexity, a novel on-board modeling method is proposed based on the Nonlinear Autoregressive with Exogenous Inputs Backpropagation neural network (NARX-BPNN) trained using the mini-batch Levenberg–Marquardt (LM) algorithm on large Quick Access Recorder (QAR) data. The NARX model’s features and time delay are chosen by referring to the line interpolation model, which gives interpretability for feature selection. The combination of a shallow neural network and big data training can guarantee the on-board model’s real-time and storage requirements, as well as its generalizability. The mini-batch LM method can avoid both the local optimum problem in the shallow neural network and the storage difficulty associated with massive data while still achieving a rapid convergence rate due to the LM algorithm’s global view. The NARX-BPNN models are compared to an existing line interpolation model using 100 different aero-engines' QAR data. The results reveal that accuracy may be increased by approximately 30% while maintaining superior dynamic performance and anti-noise capacity compared to the line interpolation approach.

show abstract

A modeling method for aero-engine by combining stochastic gradient descent with support vector regression

Cited by 47 publications

References 12 publications

Effort prediction for the software project construction phase

Effort prediction for the software project construction phase

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Aero-engine on-board model based on big Quick Access Recorder data

Contact Info

Product

Resources

About