Prediction Method of Equipment Degradation State Based on Improved RVM

In order to improve the prediction accuracy of the relevance vector machine model, an improved method for equipment condition prediction is proposed. First of all, an improved kernel function of variance Gauss kernel (VGKF) is constructed to improve the global performance and generalization ability of the kernel function. Then, by using the method of selecting the number of adjacent points in the chaotic sequence local prediction method, the H-Q criterion was used to optimize the embedding dimension of the training space to avoid the blindness of subjective selection. Through the prediction example of terminal guidance radar equipment test parameters, the effectiveness and superiority of the improved RVM were verified.

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Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2015/154703 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Qichun Bing ◽

Bowen Gong ◽

Zhaosheng Yang ◽

Qiang Shang ◽

Xiyang Zhou

Keyword(s):

Traffic Flow ◽

Kernel Function ◽

Prediction Method ◽

Relevance Vector Machine ◽

Guidance System ◽

Short Term ◽

Machine Model ◽

Traffic Flow Prediction ◽

Local Prediction ◽

Flow Prediction

Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.

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A Relevance Vector Machine Prediction Method Based on the Biased Wavelet Kernel Function

ITM Web of Conferences ◽

10.1051/itmconf/20171205002 ◽

2017 ◽

Vol 12 ◽

pp. 05002

Author(s):

Fang Liu ◽

Fei Zhao ◽

Zhen-Hao Yu ◽

Cui Zhang

Keyword(s):

Kernel Function ◽

Prediction Method ◽

Relevance Vector Machine ◽

Wavelet Kernel Function

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River Water Level Prediction in Coastal Catchment using hybridized relevance vector machine model with improved grasshopper optimization

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126477 ◽

2021 ◽

pp. 126477

Author(s):

Hai Tao ◽

Najah Kadhim Al-Bedyry ◽

Khaled Mohamed Khedher ◽

Shamsuddin Shahid ◽

Zaher Mundher Yaseen

Keyword(s):

Water Level ◽

River Water ◽

Relevance Vector Machine ◽

Machine Model ◽

Grasshopper Optimization ◽

Water Level Prediction

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The improved relevance vector machine model for crest settlement of concrete faced rock-fill dam during initial impound period

Mechanics and Mechatronics (ICMM2015) ◽

10.1142/9789814699143_0036 ◽

2015 ◽

Author(s):

Wei-Hua Fang ◽

Jian-Kun Wu ◽

Na Zhang

Keyword(s):

Relevance Vector Machine ◽

Machine Model ◽

Rock Fill

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Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.952.311 ◽

2014 ◽

Vol 952 ◽

pp. 311-314

Author(s):

Xu Sheng Gan ◽

Can Yang ◽

Hai Long Gao

Keyword(s):

Feature Extraction ◽

Least Squares ◽

Partial Least Squares ◽

Relevance Vector Machine ◽

Partial Least Square ◽

Least Square ◽

Machine Model ◽

Prediction Of Properties ◽

Kernel Partial Least Squares ◽

Engineering Materials

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

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Rolling Bearing Fault Diagnosis and Prediction Method Based on Gray Support Vector Machine Model

2015 International Conference on Computer Science and Mechanical Automation (CSMA) ◽

10.1109/csma.2015.69 ◽

2015 ◽

Cited By ~ 1

Author(s):

Jianhua Wang ◽

Taiti Kang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector Machine Model ◽

Prediction Method ◽

Rolling Bearing ◽

Support Vector ◽

Machine Model ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A Respiratory Motion Prediction Method Based on Improved Relevance Vector Machine

Mobile Networks and Applications ◽

10.1007/s11036-020-01610-7 ◽

2020 ◽

Vol 25 (6) ◽

pp. 2270-2279

Author(s):

Qi Fan ◽

Xiaoyang Yu ◽

Yanqiao Zhao ◽

Shuang Yu

Keyword(s):

Respiratory Motion ◽

Prediction Method ◽

Relevance Vector Machine ◽

Motion Prediction

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Comprehensive evaluation of robotic global performance based on modified principal component analysis

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419896881 ◽

2020 ◽

Vol 17 (4) ◽

pp. 172988141989688

Author(s):

Liming Li ◽

Jing Zhao ◽

Chunrong Wang ◽

Chaojie Yan

Keyword(s):

Principal Component Analysis ◽

Dimension Reduction ◽

Kernel Function ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Polynomial Kernel ◽

Analysis Method ◽

Principal Component Analysis Method ◽

Global Performance

The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly used. Because of the diversity and correlation of robotic global performance indexes, the two multivariate statistical methods principal component analysis and kernel principal component analysis methods can be used, respectively, to comprehensively evaluate the global performance of PUMA560 robot with different dimensions. When using the kernel principal component analysis method, the kernel function and parameters directly have an effect on the result of comprehensive performance evaluation. Because kernel principal component analysis with polynomial kernel function is time-consuming and inefficient, a new kernel function based on similarity degree is proposed for the big sample data. The new kernel function is proved according to Mercer’s theorem. By comparing different dimension reduction effects of principal component analysis method, the kernel principal component analysis method with polynomial kernel function, and the kernel principal component analysis method with the new kernel function, the kernel principal component analysis method with the new kernel function could deal more effectively with the nonlinear relationship among indexes, and its calculation result is more reasonable for containing more comprehensive information. The simulation shows that the kernel principal component analysis method with the new kernel function has the advantage of low time consuming, good real-time performance, and good ability of generalization.

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State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211042009 ◽

2021 ◽

pp. 014233122110420

Author(s):

Shuai Wang ◽

Xiaochen Zhang ◽

Wengxiang Chen ◽

Wei Han ◽

Shoubin Zhou ◽

...

Keyword(s):

Lithium Ion Battery ◽

Optimization Algorithm ◽

Prediction Method ◽

Lithium Ion ◽

Relevance Vector Machine ◽

Whale Optimization Algorithm ◽

Data Sets ◽

State Of Health ◽

Whale Optimization ◽

Maximal Relevance

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

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