This study addresses the problem of modeling the electricity demand loads in Greece. The
provided actual load data is deseasonilized and an AutoRegressive Moving Average …

Effective modeling and forecasting requires the efficient use of the information contained in
the available data so that essential data properties can be extracted and projected into the …

Convolution is the most important operation that describes the behavior of a linear time-
invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse …

A predictive modular neural network (PREMONN) architecture for time series classification is
presented. The PREMONN has a hierarchical structure. The bottom level consists of a bank …

In this paper, we propose a new radar tracking algorithm based on the Gaussian sum filter.
To alleviate the computational burden associated with the Gaussian sum filter, we have …

For the simplicity of implementation and saving of operation time, the fixed-order optimal
deconvolution filter design is appealing for engineers in signal processing from practical …

We present a new model relating cylinder combustion pressure to crankshaft angular
velocity in an internal combustion engine. There are three aspects to this model. First, by …

The subject of this book is predictive modular neural networks and their ap plication to time
series problems: classification, prediction and identification. The intended audience is …

In this paper, the multimodel partitioning theory is combined with genetic algorithms to
produce a new generation of multimodel partitioning filters, whose structure varies to …

We apply the partition algorithm to the problem of time-series classification. We assume that
the source that generates the time series belongs to a finite set of candidate sources …