Title :
An adaptive intelligent model for nucleotide sequence forecasting
Author :
Nastac, Iulian ; Tuduce, Rodica
Author_Institution :
Electron. Dept., Univ. Politeh. of Bucharest, Bucharest, Romania
Abstract :
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.
Keywords :
biocomputing; forecasting theory; genomics; neural nets; optimisation; principal component analysis; ANN; DNA genomic sequences; adaptive intelligent model; artificial neural network; nonstationary sequence; nucleotide sequence forecasting; principal components; Predictive models;
Conference_Titel :
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on
Conference_Location :
Limassol
Print_ISBN :
978-1-4244-6285-8
DOI :
10.1109/ISCCSP.2010.5463295
