this method enhances the stability of the Gaussian process regression model by using the Bagging algorithm. In addition, the basis model in the Bagging framework is model-free. The simulation data analysis shows that the proposed model has smaller mean errors, relative errors and absolute errors re...
this paper proposes a novel and efficient algorithm to predict the battery capacity trajectory in a multi-cell setting.The proposed method is a new variant of Gaussian process regression(GPR)model,and it utilizes similar trajectories in the historical data to enhance the prediction of desired ...
A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically ...
Utilising this adaptive strategy, the Gaussian process based stochastic model predictive control (GP㏒MPC) algorithm is designed by applying the adaptive tightened constraints in all prediction horizons. To reduce the computation load, the one﹕tep GP㏒MPC algorithm is further developed by imposing the...
evaluation,theproposedMA—GPRmodelsignificantlyimprovedthepredictionaccuracy ,comparedwiththeconjugategradient methodandthegeneticalgorithmoptimizationprocess. Keywords:Gaussianprocess;hyper-parametersoptimization;memeticalgorithm;regressionmodel lIntr0ducti0n Beinganewkernelmethoddevelopedonthebasis ...
Focusing on the fusion between rough fuzzy system and very scarce noisy samples, a simple but effective re-sampling algorithm based on piecewise relational transfer interpolation is presented and it is integrated with Gaussian processes regression (GPR) for WEDM process modeling. First, by using re-...
Then, local GPR models are built for each of the LDs and further integrated as finite mixture Gaussian process regression (FMGPR) models through FMM. Next, a Genetic Algorithm (GA) based ensemble pruning strategy is used to select the highly influential FMGPR models. When an estimation is ...
We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and ...
Regression results from our EM implementation on synthetic and real data-sets are given in Section 4. Results from competing approaches are also provided. Section 5 contains a discussion on the advantages and disadvantages of EM algorithm. In Section 6, a non-linear dynamic process model is ...
Hence, a good model should have low values of both of the above metrics. 2.1. Gaussian process regression This section gives an overview of Gaussian process regression. For simplicity, our presentation assumes the inputs and outputs are scalar, since we only consider 1-D capacity vs. cycle ...