An adaptive intelligent model for nucleotide sequence forecasting

The paper presents an adaptive retraining procedure that starts from a previously trained artificial neural network (ANN). The system is retrained to learn the evolution of a non-stationary sequence, without forgetting completely the previously learned data. The optimal ANN architecture is selected and the set of delayed input vectors is replaced with their principal components. The method is used for analyzing DNA genomic sequences.