  A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is
described. An ECNN starts to learn with one input node and then adding new
inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly
minimal number of input and hidden neurons as well as connections. The
algorithm was successfully applied to classify artifacts and normal segments in
clinical electroencephalograms (EEGs). The EEG segments were visually labeled
by EEG-viewer. The trained ECNN has correctly classified 96.69% of the testing
segments. It is slightly better than a standard fully connected neural network.

Neural and Evolutionary Computing;Artificial Intelligence