Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast
learning of neural networks. Families of parameterized transfer functions provide flexible …

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD)
has become routine over the past decade, utilizing microelectrode recordings to ensure …

Although computational intelligence (CI) covers a vast variety of different methods it still
lacks an integrative theory. Several proposals for CI foundations are discussed: computing …

The creation of intelligent video game controllers has recently become one of the greatest
challenges in game artificial intelligence research, and it is arguably one of the fastest …

An important, open problem in neuroimaging analyses is developing analytical methods that
ensure precise inferences about neural activity underlying fMRI BOLD signal despite the …

Neural networks use their hidden layers to transform input data into linearly separable data
clusters, with a linear or a perceptron type output layer making the final projection on the line …

The objective of this article is to find out the influence of the parameters of the ARIMA-
GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type …

Learning problems with inherent non-separable Boolean logic is still a challenge that has
not been addressed by neural or kernel classifiers. The k-separability concept introduced …

Most adaptive systems are homogenous, ie, they are built from processing elements of the
same type. MLP neural networks and decision trees use nodes that partition the input space …