Observation Stability and Convergence for Neural-type Evolutionary Variational Inequalities
Author(s):
Stefan Reitmann
Freiberg University of Mining and Technology,
Institute of Computer Science, Germany
Chair of Virtual Reality and Multimedia
Doctor of Engineering
Technological University Bergakademie Freiberg
Akademiestrasse 6
09599 Freiberg,Germany
stefan.reitmann@informatik.tu-freiberg.de
Bernhard Jung
Freiberg University of Mining and Technology,
Institute of Computer Science,Geremany
Chair of Virtual Reality and Multimedia
Doctor of Science
Professor
Technological University Bergakademie Freiberg
Akademiestrasse 6
09599 Freiberg,
Germany
jung@informatik.tu-freiberg.de
Elena V. Kudryashova
Department of Applied Cybernetics,
St. Petersburg State University
Doctor of Physical and Mathematical Sciences,
Leading Researcher
198504 St.Peterburg Starii Petergof
Universitetski pr.28
e.kudryashova@spbu.ru
Volker Reitmann
Department of Applied Cybernetics,
St. Petersburg State University
Doctor of Physical and Mathematical Sciences,
Professor
198504 St.Peterburg Starii Petergof
Universitetski pr.28
vreitmann@aol.com
Abstract:
We derive absolute observation stability and instability results for controlled
evolutionary inequalities which are based on frequency-domain characteristics of
the linear part of the inequalities. The uncertainty parts of the inequalities
(nonlinearities which represent external forces and constitutive
laws) are described by certain local and integral quadratic constraints.
Other terms in the considered evolutionary inequalities represent contact-type
properties of a mechanical system with dry friction.
The absolute stability criteria with respect to a class of observation
operators (or measurement operators) gives the opportunity to prove the weak convergence of arbitrary solutions
of inequalities to their stationary sets.
In particular the obtained results can be used for the investigation of
continuum type memories in neural networks.
In such a way we can introduce a new type of neurons with hysteretic
nonlinearities which are described by evolutionary variational inequalities.
Keywords
- Absolute observation stability
- evolutionary variational inequalities
- frequency-domain conditions
- hysteretic neural networks
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