Adaptive structures with algebraic loops

Author

Lamego, Marcelo Malini

Author_Institution

Masimo Corp., Irvine, CA, USA

Volume

12

Issue

1

fYear

2001

fDate

1/1/2001 12:00:00 AM

Firstpage

33

Lastpage

42

Abstract

The contraction theorem has many fields of application, including linear algebraic equations, differential and integral equations, control systems theory, optimization, etc. The paper aims at showing how contraction mapping can be applied to the computation and the training of adaptive structures with algebraic loops. These structures are used for the approximation of unknown functional relations (mappings) represented by training sets. The technique is extended to multilayer neural networks with algebraic loops. Application of a two-layer neural network to breast cancer diagnosis is described

Keywords

Lyapunov methods; adaptive systems; algebra; function approximation; learning (artificial intelligence); multilayer perceptrons; set theory; adaptive structures; algebraic loops; breast cancer diagnosis; contraction mapping; contraction theorem; multilayer neural networks; two-layer neural network; Adaptive systems; Approximation methods; Breast cancer; Control systems; Delay; Differential algebraic equations; Integral equations; Multi-layer neural network; Neural networks; Neurofeedback;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.896794

Filename

896794