Machine learning (ML)-based parameter estimation and classification have been receiving great attention in data modelling and processing. The Gaussian mixture model (GMM) is a probabilistic model that represents the presence of subpopulations, which works well with a parameter estimation strategy. In ...
Machine learning based decision making for time varying systems: Parameter estimation and performance optimizationMachine learningModel predictive controlTime varying systemThe class of decision making problems focuses on the optimization of single or multiple design objectives, and the classical decision ...
TheStatistics and Machine Learning Toolbox™supports these and similar parameter estimation tasks with more than 40 different probability distributions, including normal, Weibull, gamma, generalized Pareto, and Poisson. The toolbox also supportslinearandnonlinear regression. Dynamic Models Engineers apply p...
Extreme learning machinereservoir parameter estimationsandstone reservoirThis study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by ...
The Mathematics of Statistical Machine Translation: Parameter Estimation The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263-312.Brown, P.F., Della Pietra, S., Della Pietra, V., Mercer, R.L.: The Mathematics of Statistical Machine Translatio...
aTwo papers highlight fundamental techniques within machine learning,namely feature selection for regression problems and parameter estimation of dynamical systems. 二张纸在机器学习,即特征选择为退化问题和动力学系统的参量估计之内突出根本技术。[translate]...
Sparse Bayesian learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model's performance, but they are often difficult to estimate due to the nonconvexity and the...
Supplementary Fig.S2). When we connected the trained surrogate model to the parameter estimation network, the weights of the surrogate model were frozen and prevented from updating by backpropagation, but the gradient information could pass through. This was implemented in the PyTorch deep learning ...
Parameter estimation was implemented using the MATLAB function lsqcurvefit , an unconstrained Levenberg-Marquardt algorithm. Two-dimensional simulations For the 2D simulations defined by Eq. (3), additional parameters (T1,1,T1,2) are introduced and need to be determined. The analytical and MC ...
machine learning; inverse problem; harmful substances in the atmosphere; parameter estimation; atmospheric turbulence; finite element method1. Introduction Outdoor air pollution has become a serious environmental problem that has a significant impact on public health, climate change and the health of ...