Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Outlier detection refers to the identification of rare items that are deviant from the general
data distribution. Existing approaches suffer from high computational complexity, low …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Support vector machines (SVMs) are versatile learning models which are used for both
classification and regression. Several authors have reported successful applications of SVM …

Support vector machines (SVM) have drawn wide attention for the last two decades due to
its extensive applications, so a vast body of work has developed optimization algorithms to …

The large number of parameters involved in each step of Li-ion electrode manufacturing
process as well as the complex electrochemical interactions in those affect the properties of …

Noisy data and outliers has always been one of the main challenges in regression
applications. The presence of these data among training data will produce several negative …

A typical challenge for construction of accurate soft sensors in the process industries is that
industrial process data often contains various noise and outliers. Inspired by correntropy in …

In this paper, a self-organizing fuzzy neural network with hierarchical pruning scheme
(SOFNN-HPS) is proposed for nonlinear systems modelling in industrial processes. In …

Recently, a robust least squares support vector machine (R-LSSVM) was proposed, but its
computational complexity is very high compared with the traditional least squares support …