Relevance Vector Machine Model Research Articles

Wavelet based relevance vector machine model for monthly runoff prediction

Abstract In this study, wavelet transform (WT) and a relevance vector machine (RVM) are integrated to predict monthly runoff. First, the WT method is adopted to decompose the monthly runoff time series into subsequences of different scales, and the variation characteristics, especially the periodicity of the runoff, are analyzed. Then, the regression model of RVM is established in each subsequence. Finally, the prediction results of each subsequence are integrated to obtain the final predicted values of monthly runoff through wavelet inverse transform. The proposed model was tested using the historical data of Minjiang River; the results show that compared with the RVM model, the WT-RVM model has better precision and can be applied in the prediction of monthly runoff.

Measurement of Zone Temperature Profile of a Resistive Heating Furnace Through RVM Model

In this paper, a system for measuring the zone temperature profile of a horizontal tube furnace is proposed. This system consists of an ultrasonic transducer, quartz tube, resistive horizontal tube furnace, Arduino micro-controller and relevance vector machine (RVM)-based regression model. The ultrasonic transducer is placed in such a way that the acoustic wave is transmitted and then reflected back from the resistive horizontal tube furnace. The variations in the temperature values inside the tube furnace may affect the receiving voltage of the ultrasonic transducer. The RVM model is employed to predict the temperature using the inputs as the ultrasonic transducer voltage and the distance of the propagating path of the ultrasonic wave. The experimental results demonstrate that the proposed system is effective to measure the temperature inside a horizontal furnace and it shows a correlation coefficient value of 0.9695 with the K -type thermocouple readings. Such a system can be employed in an industrial furnace to measure the temperature with a higher accuracy.

Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods

Curie temperature (Tc), the second order phase transition temperature, is also one of the important physical properties of perovskite materials. It is a meaningful work to quickly and efficiently predict Tc of new perovskite materials before doing a considerable amount of experimental work. In the work, SVM (support vector machine), RVM (relevance vector machine) and RF (random forest) were employed to establish the prediction models of Tc with the physicochemical parameters, respectively. The results reveal that the three models all have high precision and reliability. According to K-fold cross validation, the SVR model had better prediction performance than the RVM and RF models. Meanwhile, the potential perovskite material with higher Tc was found by using the SVR model integrated with the search strategy of genetic algorithm from the virtual samples. The methods outlined here can provide valuable hints into the exploration of materials with desired property and can accelerate the process of materials design.

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

Relevance vector machine is a newly proposed and effective state prediction algorithm proved by practical applications; however, the accuracy of the single relevance vector machine model for the long-term prediction is unable to achieve satisfactory results with time goes by. Then, an autoregressive integrated moving average model is introduced to correct the prediction error caused by the single relevance vector machine, and a fusion framework based on the combination of relevance vector machine and autoregressive integrated moving average model is adopted to improve the accuracy of long-term prediction. In addition, a targeted approach for retraining the old model is put forward so that the state prediction model can be updated in time and suits the actual situation better. The effectiveness of the proposed fusion framework is illustrated via an aircraft actuator, and the experiments based on a model of civil aircraft actuator data set show that the proposed method yields a satisfied performance in state prediction of aircraft actuators.

Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar

The built environment and human life have been regularly affected by earthquakes. One of the most catastrophic earthquake hazards is liquefaction causing failure of embankment, earth structures, foundations, natural slopes and superstructures. In this paper, three methods have been used to predict the liquefaction susceptibility of some of high seismically active regions of Bihar such as Darbhanga, Samastipur, West-Champaran and Araria Sangram. The first method is based on semi-empirical approach, given by Idriss and Boulanger (in: Proceedings of 11th International conference on soil dynamics and earthquake engineering and 3rd International conference on earthquake geotechnical engineering vol 1, pp 32–56, 2006). The stress reduction factor (rd), overburden correction factor for cyclic stress ratios Kσ, earthquake magnitude scaling factor for cyclic stress ratios (MSF) and recently modified relations has been considered in this approach. The result is obtained in form of factor of safety at particular depth beneath the ground surface. The second approach is based on Muduli’s probabilistic method (2013) and third is on relevance vector machine (RVM) method developed by Karthikeyan et al. (Eur J Environ Civ Eng 17(4):248–262, 2013). RVM model has been used to determine the liquefaction potential of soil based on standard penetration test (SPT) blow count. The SPT data has been used as input parameter for the analysis. Site coming under high seismic zone is more susceptible to liquefaction during earthquake. This concludes that earthquake magnitude plays very important role for the assessment of liquefaction, irrespective of soil type and water table of the site. This paper contains the deterministic and probabilistic analysis of liquefaction behavior of soil.

The Probabilistic Model and Forecasting of Power Load Based on Variational Bayesian Expectation Maximization and Relevance Vector Machine

As the surging demands of secure power supply and reliable power system, the power load approximation and forecasting are becoming more significant and more important. Different from the current research work, we integrate power load approximation and forecasting based on the Gaussian mixture model and relevance vector machine. In order to estimate the parameters of GMM, the variational bayesian expectation maximization algorithm are employed. Based on the estimation results, the relevance vector machine and bayesian regression model are built to forecast the power load and its labels. The simulation results show that the proposed algorithms can closely approximate the power load profiles and forecast the power load with lower errors.

Prediction of wear trend of engines via on-line wear debris monitoring

Abstract On-line wear debris monitoring is a useful technology for real-time machine wear condition monitoring but needs further development. This study, based on previous developments of an on-line visual ferrograph (OLVF), focused on (i) data reconstruction for extracting representative and reliable wear condition related characteristics, and (ii) development of an improved model for on-line wear prediction. Wear monitoring of a diesel engine was performed using this on-line wear debris monitoring system. Experimental results and comparisons between the improved relevance vector machine (RVM) model and other models show that the improved RVM model gives an earlier warning and enhances the prediction accuracy.

Rotor Position Self-Sensing of SRM Using PSO-RVM

The motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.

Prediction of compressive strength of GGBS based concrete using RVM

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

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.

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

Abstract The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.

Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB.

Probabilistic Prediction of Bus Headway Using Relevance Vector Machine Regression

Bus headway regularity heavily affects transit riders’ attitude for choosing public transportation and also serves as an important indicator for transit performance evaluation. Therefore, an accurate estimate of bus headway can benefit both transit riders and transit operators. This paper proposed a relevance vector machine (RVM) algorithm to predict bus headway by incorporating the time series of bus headways, travel time, and passenger demand at previous stops. Different from traditional computational intelligence approaches, RVM can output the probabilistic prediction result, in which the upper and lower bounds of a predicted headway within a certain probability are yielded. An empirical experiment with two bus routes in Beijing, China, is utilized to confirm the high precision and strong robustness of the proposed model. Five algorithms [support vector machine (SVM), genetic algorithm SVM, Kalman filter, k-nearest neighbor, and artificial neural network] are used for comparison with the RVM model and the result indicates that RVM outperforms these algorithms in terms of accuracy and confidence intervals. When the confidence level is set to 95%, more than 95% of actual bus headways fall within the prediction bands. With the probabilistic bus headway prediction information, transit riders can better schedule their trips to avoid late and early arrivals at bus stops, while transit operators can adopt the targeted correction actions to maintain regular headway for bus bunching prevention.

Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component Analysis and Linear Discriminant Analysis

Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic. Compared to support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM. However, the classification accuracy of RVM is not high when apply to hyperspectral data. A novel classification method based on RVM is presented in this paper. The method combine principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimensionality of hyperspectral data. Firstly, PCA is used to the first dimension reduction and obtain nonsingular intra-class scatter matrix. Secondly, LDA is applied to the second dimension reduction and reduce the amounts of computation great. Finally, the relevance vector machine model is applied to remote sensing image classification. The hyperspectral data of 1992 Indian Pines has been used in this paper. The experimental results show that the proposed method not only improves overall accuracy than RVM and PCA-RVM, but also extend the ratio between inter-class distances and intra-class distances.

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO) algorithm and relevance vector machine(RVM). RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.

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

Relevance Vector Machine (RVM) is an important learning method in the field of machine learning for its sparsity, global optimality and the ability to solve nonlinear problems by using kernel functions. In this paper, a family of biased wavelets was used to construct the kernel functions of RVM. Biased wavelet have adjustable nonzero mean which makes the kernel of RVM more flexible. With the kernel method of the Centered Kernel Target Alignment (CKTA), the biased parameter was selected to improve the prediction performance of RVM model. The algorithm based on the biased wavelet kernel showed an increased prediction accuracy compared to using wavelet kernel and Cauchy kernel. In short, Relevance Vector Machine with the biased wavelet kernel is a flexible prediction algorithm with high prediction accuracy.

Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model

Long-term streamflow forecasting is crucial to reservoir scheduling and water resources management. However, due to the complexity of internally physical mechanisms in streamflow process and the influence of many random factors, long-term streamflow forecasting is a difficult issue. In the article, we mainly investigated the ability of the Relevance Vector Machine (RVM) model and its applicability for long-term streamflow forecasting. We chose the Dahuofang (DHF) Reservoir in Northern China and the Danjiangkou (DJK) Reservoir in Central China as the study sites, and selected the 500 hpa geopotential height in the northern hemisphere and the sea surface temperatures in the North Pacific as the predictor factors of the RVM model and the Support Vector Machine (SVM) model, and then conducted annual streamflow forecasting. Results indicate that forecasting results in the DHF Reservoir is much better than that in the DJK Reservoir when using SVM, because streamflow process in the latter basin has a magnitude bigger than 1000 m3/s. Comparatively, accurate forecasting results in both the two basins can be gotten using the RVM model, with the Nash Sutcliffe efficiency coefficient bigger than 0.7, and they are much better than those gotten from the SVM model. As a result, the RVM model can be an effective approach for long-term streamflow forecasting, and it also has a wide applicability for the streamflow process with a discharge magnitude from dozen to thousand cubic meter per second.