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Structure of Principal Component Based Neural Network Models of Dynamic Systems

Želimir Kurtanjek ; Faculty of Food Technology and Biotechnology, University of Zagreb, Croatia

Puni tekst: engleski, pdf (4 MB) str. 1-8 preuzimanja: 53* citiraj
APA 6th Edition
Kurtanjek, Ž. (1995). Structure of Principal Component Based Neural Network Models of Dynamic Systems. Journal of computing and information technology, 3 (1), 1-8. Preuzeto s https://hrcak.srce.hr/150435
MLA 8th Edition
Kurtanjek, Želimir. "Structure of Principal Component Based Neural Network Models of Dynamic Systems." Journal of computing and information technology, vol. 3, br. 1, 1995, str. 1-8. https://hrcak.srce.hr/150435. Citirano 08.03.2021.
Chicago 17th Edition
Kurtanjek, Želimir. "Structure of Principal Component Based Neural Network Models of Dynamic Systems." Journal of computing and information technology 3, br. 1 (1995): 1-8. https://hrcak.srce.hr/150435
Harvard
Kurtanjek, Ž. (1995). 'Structure of Principal Component Based Neural Network Models of Dynamic Systems', Journal of computing and information technology, 3(1), str. 1-8. Preuzeto s: https://hrcak.srce.hr/150435 (Datum pristupa: 08.03.2021.)
Vancouver
Kurtanjek Ž. Structure of Principal Component Based Neural Network Models of Dynamic Systems. Journal of computing and information technology [Internet]. 1995 [pristupljeno 08.03.2021.];3(1):1-8. Dostupno na: https://hrcak.srce.hr/150435
IEEE
Ž. Kurtanjek, "Structure of Principal Component Based Neural Network Models of Dynamic Systems", Journal of computing and information technology, vol.3, br. 1, str. 1-8, 1995. [Online]. Dostupno na: https://hrcak.srce.hr/150435. [Citirano: 08.03.2021.]

Sažetak
A new structure of neural network based systems for modeling and control of dynamic industrial processes is developed. The structure is composed of three serially connected subsystems: 1) ARMA - auto regression moving averages to account for system dynamics; 2) PCA - principal component analysis as statistical preprocessor for noise reduction and pattern compression 3) ANN - artificial neural network with static neurons and feed forward pattern propagation for nonlinear mapping of input/output interaction. Training of neural networks is performed with Ribiera-Polack-Powell conjugate gradient method for minimization of the variance in output patterns between a real and a model system. The proposed modeling procedure is applied to data from a fed batch operation of an industrial deep jet bioreactor. Predictive power of the model is based on the analysis of responses in pseudosteady and oscillatory mode of operation with the trained and untrained patterns. The aim of the work is to develop a general neural network structure and analyze its applicability in the process control in biotechnology.

Ključne riječi
auto regression; principal component decomposition; neural networks; process modeling; process control; biotechnology

Hrčak ID: 150435

URI
https://hrcak.srce.hr/150435

Posjeta: 115 *