Parametric system identification, which is the process of uncovering the inherent dynamics
of a system based on the model built with the observed inputs and outputs data, has been …

A novel, hybrid structure for week-ahead load forecasting is presented. It is the energy
market evolution that compels its participants to require load predictions whose accuracy …

Reliable and accurate short-term subway passenger flow prediction is important for
passengers, transit operators, and public agencies. Traditional studies focus on regular …

Short-term passenger flow prediction and passenger flow control are essential for managing
congestion in metros. This paper proposes a new dynamic radial basis function (RBF) …

The automatic detection of epileptic seizures from electroencephalography (EEG) signals is
crucial for the localization and classification of epileptic seizure activity. However, seizure …

The set of available multi-objective optimisation algorithms continues to grow. This fact can
be partially attributed to their widespread use and applicability. However, this increase also …

This paper proposes the multi-kernel neural networks and applies them to model the
nonlinear unsteady aerodynamics at constant or varying flow conditions. Different from …

The on-line prediction of ship roll motion during maneuvering plays an important role in
navigation safety and ship control applications. This paper presents an on-line prediction …

The presence of Missing Values in a data set can affect the performance of a classifier
constructed using that data set as a training sample. Several methods have been proposed …

This paper introduces a modified version of the Hybrid Multilayer Perceptron (HMLP)
network to improve the performance of the conventional HMLP network. We adopted the …