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 …

Artificial neural networks as a major soft-computing technology have been extensively
studied and applied during the last three decades. Research on backpropagation training …

A new generation learning system based on recent advances in statistical learning theory
deliver state-of-the-art performance in real-world applications that is Support Vector …

Forecasting crude oil price is a challenging task. Given the nonlinear and time-varying
characteristics of international crude oil prices, we propose a novel hybrid method to …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

Abstract System modeling is a vital part of building energy optimization and control. Grey
and white box modeling requires knowledge about the system and a lot of human …

In this paper, a novel adaptive stable backstep** controller (BSC) based on support vector
regression (SVR) has been introduced for nonlinear dynamical systems. Stable BSC is …

Support Vector Machines, a new generation learning system based on recent advances in
statistical learning theory deliver state-of-the-art performance in real-world applications such …

Abstract Characterization of the botanical origin and quality of honeys is of great importance
and interest in agriculture. In this study, an electronic nose (e-nose) was applied for …

In this paper, a prediction model is developed using support vector machine for forecasting
the parameter associated with ground motion of a seismic signal. The prediction model is …