This paper provides an overview of the support vector machine (SVM) methodology and its
applicability to real‐world engineering problems. Specifically, the aim of this study is to …

The identification of non-linear systems using only observed finite datasets has become a
mature research area over the last two decades. A class of linear-in-the-parameter models …

This paper reports on a modelling study of new solar air heater (SAH) system efficiency by
using least-squares support vector machine (LS-SVM) method. In this study, a device for …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

This paper reports on a modeling study of ground coupled heat pump (GCHP) system
performance (COP) by using a support vector machine (SVM) method. A GCHP system is a …

Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been
investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, PH (2008). L1 …

System identification combined with free-running model tests or full-scale trials is one of the
effective methods to determine the hydrodynamic coefficients in the mathematical models of …

Abstract Model Predictive Control is a well consolidated technique to design optimal control
strategies, leveraging the capability of a mathematical model to predict a system's behavior …

This paper describes the use of Kernel Ridge Regression (KRR) and Kernel Ridge
Regression Confidence Machine (KRRCM) for black box identification of a surface marine …

A locally regularized orthogonal least squares (LROLS) algorithm is proposed for
constructing parsimonious or sparse regression models that generalize well. By associating …