EAN: 9789811029134

PRINCIPAL COMPONENT ANALYSIS NETWORKS AND ALGOR...

This book not only provides a comprehensive introduction to neural-based PCA methods in control
science  but also presents many novel PCA algorithms and their extensions and generalizations 
e.g.  dual purpose  coupled PCA  GED  neural based SVD algorithms  etc. It also discusses in
detail various analysis methods for the convergence  stabilizing  self-stabilizing property of
algorithms  and introduces the deterministic discrete-time systems method to analyze the
convergence of PCA MCA algorithms. Readers should be familiar with numerical analysis and the
fundamentals of statistics  such as the basics of least squares and stochastic algorithms.
Although it focuses on neural networks  the book only presents their learning law  which is
simply an iterative algorithm. Therefore  no a priori knowledge of neural networks is required.
This book will be of interest and serve as a reference source to researchers and students in
applied mathematics  statistics  engineering  and other related fields.

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