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3 results about "Relevance vector machine" patented technology
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In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification. It is actually equivalent to a Gaussian process model with covariance function: k(𝐱,𝐱ʼ)=∑ⱼ₌₁ᴺ1/αⱼφ(𝐱,𝐱ⱼ)φ(𝐱ʼ,𝐱ⱼ) where φ is the kernel function (usually Gaussian), αⱼ are the variances of the prior on the weight vector w∼N(0,α⁻¹I), and 𝐱₁,…,𝐱N are the input vectors of the training set.
A method and system for predicting the remaining life of a rotating machine
The application relates to a rotating machine residual life prediction method and system, and belongs to the field of mechanical life prediction. The method comprises the following steps: collecting signals capable of reflecting mechanical life of a rotating machine to be measured at least at two positions, extracting one-dimensional time characteristic data from the signals capable of reflecting mechanical life by using principal component analysis pooling, training a related vector machine by using the one-dimensional time characteristic data, so that the full life cycle of the rotating machine is divided into a period without obvious failure trend and a failure tendency period; training a deep separable convolution gate recurrent unit network by using data of the failure tendency period, and predicting the residual life of the rotating machine in the failure tendency period. The application can improve the prediction accuracy when the residual service life of the rotating machine is predicted.
Owner:HUANENG TAICANG POWER GENERATION CO LTD +1
An Incremental Correlation Vector Machine-Based Online Prediction Method for Battery State of Charge (SOC) Based on Multi-Core Integration Strategy
ActiveCN116008816BImprove estimation accuracyImprove computing efficiencyElectrical testingBattery state of chargeAlgorithm
This invention discloses an online prediction method for battery SOC based on an incremental correlation vector machine (RVM) strategy using a multi-kernel ensemble approach. The method includes the following steps: Step 1, data preprocessing; Step 2, training set sampling; Step 3, kernel function selection; Step 4, model training; Step 5, model validation; Step 6, adaptive kernel parameters; Step 7, RVM model ensemble; Step 8, model prediction; Step 9, incremental learning strategy; and Step 10, online incremental prediction. From a practical perspective, this invention addresses the complexities and diverse needs of various applications. Drawing on the ideas of incremental learning and ensemble learning, it generates highly differentiated RVM individual learning models containing multiple kernel functions through dual perturbation of training samples and kernel functions. Combined with a novel incremental ensemble strategy, it avoids the problem of model overlearning, improves the model's generalization ability and robustness, and expands its application scope.
Owner:GUILIN UNIV OF ELECTRONIC TECH