Application of polynomial cellular neural networks in diagnosis of astrometric chromaticity

• Published in: Applied mathematical modelling Vol. 34; no. 12; pp. 4243 - 4252
• Main Authors: Cancelliere, R., Gai, M., Slavova, A.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Inc 01.12.2010; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Cellular; Chromaticity; Computer science; control theory; systems; Computer simulation; Connectionism. Neural networks; Data reduction; Diagnosis; Exact sciences and technology; Image processing; Lyapunov’s finite majorizing equations; Mathematical analysis; Mathematical models; Neural networks; Pattern recognition. Digital image processing. Computational geometry; Stability analysis; Image processing; Data reduction; Lyapunov’s finite majorizing equations; Lyapunov's finite majorizing equations; Pipeline processor; Cellular neural nets; Lyapunov method; Minimization; Neural network; Real time; Modeling; Lyapunov equation; Chromaticity; Diagnosis
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2010.04.021

In this paper minimization of the chromatic error in the data reduction pipeline of the Gaia mission is presented by applying polynomial cellular neural networks (PCNN). We introduce generalized PCNN model which enables us to solve the nonlinear approximation task. The advantage of the newly proposed method is in solving large-size image processing problem of diagnosis of astrometric chromaticity in real time. Rigorous stability analysis of the PCNN is presented by using the method of Lyapunov’s finite majorizing equations. The simulation results show a linear relation between the output of the proposed PCNN model and the chromaticity values as the target data.