Accelerated convergence of neural network system identification algorithms via principal component analysis

David C. Hyland; Lawrence D. Davis; Keith K. Denoyer

doi:10.1117/12.332480

8 December 1998 Accelerated convergence of neural network system identification algorithms via principal component analysis

David C. Hyland, Lawrence D. Davis, Keith K. Denoyer

Author Affiliations +

Proceedings Volume 3430, Novel Optical Systems and Large-Aperture Imaging; (1998) https://doi.org/10.1117/12.332480
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

While significant theoretical and experimental progress has been made in the development of neural network-based systems for the autonomous identification and control of space platforms, there remain important unresolved issues associated with the reliable prediction of convergence speed and the avoidance of inordinately slow convergence. To speed convergence of neural identifiers, we introduce the preprocessing of identifier inputs using Principal Component Analysis (PCA) algorithms. Which automatically transform the neural identifier's external inputs so as to make the correlation matrix identity, resulting in enormous improvements in the convergence speed of the neural identifier. From a study of several such algorithms, we developed a new PCA approach which exhibits excellent convergence properties, insensitivity to noise and reliable accuracy.

Citation Download Citation

David C. Hyland, Lawrence D. Davis, and Keith K. Denoyer "Accelerated convergence of neural network system identification algorithms via principal component analysis", Proc. SPIE 3430, Novel Optical Systems and Large-Aperture Imaging, (8 December 1998); https://doi.org/10.1117/12.332480

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