DocumentCode
2346148
Title
An adaptive intelligent model for nucleotide sequence forecasting
Author
Nastac, Iulian ; Tuduce, Rodica
Author_Institution
Electron. Dept., Univ. Politeh. of Bucharest, Bucharest, Romania
fYear
2010
fDate
3-5 March 2010
Firstpage
1
Lastpage
4
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on
Conference_Location
Limassol
Print_ISBN
978-1-4244-6285-8
Type
conf
DOI
10.1109/ISCCSP.2010.5463295
Filename
5463295
Link To Document