An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

Yang, Yong; Tan, Minggao; Dai, Yuewei

doi:10.1371/journal.pone.0171246

Cited by 11 publications

(4 citation statements)

References 14 publications

(11 reference statements)

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“…The kernel function of the LSSVM method in this paper is the Gaussian radial basis function (RBF), and the equation is as follows [ 33 ]: where σ is the kernel function parameter. The smaller the value is, the easier it is to underfit, and the larger the value is, the easier it is to overfit.…”

Section: Methodsmentioning

confidence: 99%

An online identification approach for ship domain model based on AIS data

2022

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As an important basis of navigation safety decisions, ship domains have always been a pilot concern. In the past, model parameters were usually obtained from statistics of massive historical cumulative data, but the results were mostly historical analysis and static data, which obviously could not meet the needs of pilots who wish to master the ship domain in real time. To obtain and update the ship domain parameter online in time and meet the real-time needs of maritime applications, this paper obtains CRI as the weight coefficient-based PSO-LSSVM method and proposes to use short-term AIS data accumulation through the risk-weighted least squares method online rolling identification method, which can filter nonhazardous targets and improve the identification accuracy and real-time performance of nonlinear models in the ship domain. The experimental examples show that the method can generate the ship domain dynamically in real time. At the same time, the method can be used to study the dynamic evolution characteristics of the ship domain over the course of navigation, which provides a reference for navigation safety decisions and the analysis of ship navigation behavior.

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Section: Methodsmentioning

confidence: 99%

An online identification approach for ship domain model based on AIS data

2022

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“…Therefore, the combination of Fuzzy Comprehensive Evaluation and SVR is used to evaluate the health status of the system. The prediction effect of SVR is not good because the parameters are artificially chosen [ 25 ]. In order to further improve the prediction accuracy, CS algorithm is used to optimize the parameters (the penalty factor C and the parameter g of GRBF) of SVR, and to optimize the performance of the SVR model correspondingly.…”

Section: Evaluation Model Based On Fuzzy Comprehensive Cs-svrmentioning

confidence: 99%

“…It is necessary to use a series of process of optimization algorithm to the parameter g and penalty factor C of SVR model. Yang YF, et al used CS algorithm to optimize the parameters of LSSVM, obtained the optimization result and proved its feasibility of those means [ 14 ]. By comprehensive studying the means in the literature [ 8 ] and [ 10 ], we choose the penalty factor C and the parameter g of GRBF for SVR model.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive CS-SVR Model-based health status evaluation of radar

2019

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The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fault monitoring signal, the health status evaluation of a radar system is quite difficult. By establishing the evaluation index system of a radar, the combination of AHP method and Entropy weight method is studied in this paper. In order to evaluate the value of health status, several optimization algorithms including PSO, GA, BA and CS are used for optimizing the parameters of SVR model. Meanwhile, in order to avoid the problem that the system is at the edge of the state, a radar health assessment method based on the combination of Fuzzy Comprehensive Evaluation and Cuckoo Search-Support Vector Regression (CS-SVR), which is named as Fuzzy Comprehensive CS-SVR (FCCS-SVR), is further proposed. The result of case analysis reflects that the state evaluation of the radar system is realized. The system performance analysis shows that the use of FCCS-SVR evaluation method provides a high recognition rate and can accurately assess the health status of the radar system.

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“…LS-SVM algorithms will deal with a set of linear equations instead of a quadratic optimization problem, which reduces the computation time of model learning significantly and improves higher solution accuracy. Therefore, LS-SVM algorithms have various applications in the area of pattern recognition [38], fault diagnosis [39], and time-series prediction [40,41]. In addition, LS-SVM algorithms have been successfully applied for solving differential equations [42,43], differential algebraic equations [44,45], and integral equations [46].…”

Section: Introductionmentioning

confidence: 99%

The LS-SVM algorithms for boundary value problems of high-order ordinary differential equations

Yin

et al. 2019

Adv Differ Equ

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This paper introduces the improved LS-SVM algorithms for solving two-point and multi-point boundary value problems of high-order linear and nonlinear ordinary differential equations. To demonstrate the reliability and powerfulness of the improved LS-SVM algorithms, some numerical experiments for third-order, fourth-order linear and nonlinear ordinary differential equations with two-point and multi-point boundary conditions are performed. The idea can be extended to other complicated ordinary differential equations.

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An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

Cited by 11 publications

References 14 publications

An online identification approach for ship domain model based on AIS data

An online identification approach for ship domain model based on AIS data

A Fuzzy Comprehensive CS-SVR Model-based health status evaluation of radar

The LS-SVM algorithms for boundary value problems of high-order ordinary differential equations

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