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Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model

Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series.   (本文共11页) 阅读全文>>

《信号处理》2020年05期
信号处理

均匀先验分布Bayesian自适应波束形成方法

针对UUV舷侧阵存在观测信号波达方向估计结果有误差的情况,提出了基于均匀先验分布的Bayesian自适应波束形成方法(UB)。该方法假设期望信号的到达方向是区间内服从均匀先验分布的一个变量,利用含有均匀先验...  (本文共6页) 阅读全文>>

《Journal of Systems Engineering and Electronics》2020年03期
Journal of Systems Engineering and Electronics

Bayesian inference for ammunition demand based on Gompertz distribution

Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades, a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed. The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is intr...  (本文共11页) 阅读全文>>

《IEEE/CAA Journal of Automatica Sinica》2020年05期
IEEE/CAA Journal of Automatica Sinica

Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

Prediction intervals(PIs) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks(KDBN), serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for ...  (本文共9页) 阅读全文>>

《The Crop Journal》2020年05期
The Crop Journal

Bayesian regularized quantile regression:A robust alternative for genome-based prediction of skewed data

Genomic prediction(GP) has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle. A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself. In this study, we propose to use Bayesian regularized quantile regression(BRQR) in the context of GP...  (本文共10页) 阅读全文>>

《Earthquake Research in China》2020年03期
Earthquake Research in China

Simulation of Silty Clay Compressibility Parameters Based on Improved BP Neural Network Using Bayesian Regularization

Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human interference.When the sample size is too large,conventional methods require massive human and financial resources.In order to reasonably simulate the compressibility parameters of the sample,this paper firstly adopts the correlation analysis to select seven influe...  (本文共16页) 阅读全文>>