The radial basis function (RBF) network has its foundation in the conventional approximation
theory. It has the capability of universal approximation. The RBF network is a popular …

Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

In a deregulated power market, bidding decisions rely on good market clearing price
prediction. One of the common forecasting methods is Gaussian radial basis function …

Prognostics and Health Management aims at estimating the remaining useful life of a system
(RUL), ie the remaining time before a failure occurs. It benefits thereby from an increasing …

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The dissertation introduces method and algorithm development for nonstationary, nonlinear
and dynamic signals. Furthermore, the dissertation concentrates on applying neural …

Radial basis function (RBF) networks are widely used for modeling a function from given
input-output patterns. However, two difficulties are involved with traditional RBF (TRBF) …

We consider the problem of reconstructing bifurcation diagrams (BDs) of maps using time
series. This study goes along the same line of ideas presented by Tokunaga et al.[Physica D …

Regional strain-based brain injury intensity during controlled cortical impact (CCI) was
studied using a three-dimensional numerical rat brain model. A full factorial design of CCI …