Two‐Dimensional Temperature Field Distribution Reconstruction Based on Least Square Method and Radial Basis Function Approximation

Jia, Ruixi; Xiong, Qingyu

doi:10.1155/2017/1213605

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…Under the condition of relying on human experience and intelligence, only simple conditional judgment calculations and table lookup operations are needed. Realization, in this way, the requirements of the control algorithm on the hardware equipment are reduced, the overall computing efficiency of the system is improved, and a relatively perfect balance is found between the high-performance, high-precision control effect and the low-power, high-real-time system requirements [25]. A basic fuzzy control system includes fuzzy controller, execution unit, controlled object, controlled quantity detection transposition, and other parts [26,27], and its principle structure diagram is shown in Figure 3.…”

Section: Membership Functionmentioning

confidence: 99%

Iterative Learning‐Based PID Precision Control for Sports Performance Analysis

Shen

et al. 2021

Wireless Communications and Mobile Computing

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Traditional function algorithms are contradictory to accuracy and performance. Therefore, taking into account the balance of accuracy and performance, the research of accuracy control-oriented mathematical function algorithms is of great significance to the design of high-precision and high-performance mathematical function algorithms. This paper is aimed at the design of mathematical function algorithm for precision control and has conducted indepth research on traditional PID control algorithm and fuzzy logic control theory. By analyzing the advantages and disadvantages of the two in practical applications, a parameter fuzzy cascade PID control is designed. The algorithm and its performance simulation and comparative analysis provide a theoretical basis for the follow-up accuracy control algorithm research and realization process. The accuracy control algorithm is then used to calculate and statistically analyze the sports performance including speed, strength (comprehensiveness and explosiveness), endurance, sensitivity, and coordination. The results show that the optimized function random point (nonextreme) test calculation accuracy is 99.5%, and the control accuracy improvement rate of the parameter fuzzy cascade PID control algorithm is about 18.24%. It has better control effect, stronger stability, and higher control accuracy. In the test of extreme points, the optimized test results are obviously better than those before optimization, which can effectively calculate sports results with high accuracy.

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Section: Membership Functionmentioning

confidence: 99%

Iterative Learning‐Based PID Precision Control for Sports Performance Analysis

Shen

et al. 2021

Wireless Communications and Mobile Computing

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“…Typical reconstruction algorithms include the least square algorithm (LSA), the algebraic reconstruction technique (ART), the simultaneous iterative reconstruction technique (SIRT), and the simultaneous algebraic reconstruction technique (SART). 5,12,14,15 To mitigate the ill-posed nature of the inverse problem, these algorithms require the number of cells contained in the measured area to be less than the number of the sound paths. Thus, these methods can hardly offer entire and exact information of temperature distribution because of their limited interpolation scope.…”

Section: Introductionmentioning

confidence: 99%

“…Temperature information around the edge of the measurement region may be missing. 15 To overcome this problem, various two-phase reconstruction methods have been proposed. Jia and Xiong 15 first reconstructed the temperature distribution using the LSA on a coarse grid, and then they calculated the whole temperature distribution by a radial basis function approximation model.…”

Section: Introductionmentioning

confidence: 99%

“…15 To overcome this problem, various two-phase reconstruction methods have been proposed. Jia and Xiong 15 first reconstructed the temperature distribution using the LSA on a coarse grid, and then they calculated the whole temperature distribution by a radial basis function approximation model. Liu et al 5 first reconstructed the temperature distribution using the SART on a coarse grid, and then they predicted the temperature distribution on a refined grid by the Gaussian process regression method.…”

Section: Introductionmentioning

confidence: 99%

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Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

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“…e parameters of the grey prediction model are important factors that affect the performance of the grey prediction model. Generally, the least squares method is used to estimate the parameters [28]. Based on the residual sum of squares minimum optimization, the least squares method easily falls into the local minimum.…”

Section: Introductionmentioning

confidence: 99%

Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

Qin

Yang

et al. 2021

Mathematical Problems in Engineering

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It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production.

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Two‐Dimensional Temperature Field Distribution Reconstruction Based on Least Square Method and Radial Basis Function Approximation

Cited by 19 publications

References 15 publications

Iterative Learning‐Based PID Precision Control for Sports Performance Analysis

Iterative Learning‐Based PID Precision Control for Sports Performance Analysis

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

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