ANN Prediction of Nucleotide Sequences Link of Principal Component Analysis to Fourier Transform

Author

Cristea, Paul ; Deklerck, Rudi ; Cornelis, Jan ; Tuduce, Rodica ; Nastac, Iulian ; Andrei, Marius

fYear

2007

fDate

27-30 June 2007

Firstpage

69

Lastpage

73

Abstract

The paper presents a flexible artificial neural network (ANN) model, in order to support modifications of a complex input-output function that describes the catalyst monitoring process of a multi-tube reactor. The goal is to obtain a good accuracy of the predicted data by using an optimal ANN architecture and well-suited delay vectors. The research targets the implementation of an adaptive system, which can be periodically retrained, in order to continuously learn the latest evolution of the catalyst process.

Keywords

Fourier transforms; genetic engineering; medical computing; neural nets; principal component analysis; ANN prediction; Fourier transform; artificial neural network; catalyst process; complex input-output function; delay vectors; multi-tube reactor; nucleotide sequences; principal component analysis; Artificial neural networks; Bioinformatics; DNA; Fourier transforms; Genomics; Human immunodeficiency virus; Pathogens; Principal component analysis; Sequences; Signal processing; Genomic signals; PCA; Sequence prediction; Time series prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Image Processing, 2007 and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services. 14th International Workshop on

Conference_Location

Maribor

Print_ISBN

978-961-248-029-5

Electronic_ISBN

978-961-248-029-5

Type

conf

DOI

10.1109/IWSSIP.2007.4381155

Filename

4381155