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

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

Dec 1998

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998spie.3430..248h&link_type=abstract

Proc. SPIE Vol. 3430, p. 248-259, Novel Optical Systems and Large-Aperture Imaging, Kevin D. Bell; Michael K. Powers; Jose M. Sa

Computer Science

Scientific paper

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.

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