Upper-Triangulization of Non-Symmetric Matrices Using Sanger´s Type Learning Systems

Author

Hasan, Mohammed A.

Author_Institution

Dept. of Electr. & Comput. Eng., Minnesota Univ., Duluth, MN

fYear

2007

fDate

27-30 May 2007

Firstpage

1009

Lastpage

1012

Abstract

New minor and principal component flows are derived and analyzed in terms of global stability and properties of the limiting solutions. These systems which are of Sanger´s type are specifically explored in terms of their applicability to symmetric and non-symmetric matrices. Analytical proofs of global stability and conditions under which the limiting solutions upper-triangulize a given matrix are given.

Keywords

matrix algebra; principal component analysis; stability; Lyapunov stability; Sanger type learning systems; global convergence; global stability; minor components; nonsymmetric matrices; principal components; symmetric matrices; upper-triangulization; Eigenvalues and eigenfunctions; Lagrangian functions; Learning systems; Lyapunov method; Matrix converters; Principal component analysis; Stability analysis; Symmetric matrices; Liapunov stability; MCA/PCA for non-symmetric matrices; Oja´s learning rule; global convergence; minor components; principal components;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on

Conference_Location

New Orleans, LA

Print_ISBN

1-4244-0920-9

Electronic_ISBN

1-4244-0921-7

Type

conf

DOI

10.1109/ISCAS.2007.378140

Filename

4252808