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Modeling and classification of approximately periodic signals by chaotic systems

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Abstract:
Instead of modeling the diversity of a signal class by a stochastic process, we propose to use the diversity of the trajectories within a chaotic attractor for this purpose. From a given set of prototype signals of the class, the learning set, the nonlinear dynamic model is obtained by system identification. It is remarkable that this process automatically leads to a chaotic system. Once such a model is established, the recognition of a signal belonging to the class, or the rejection of a signal not belonging to the class, is performed by synchronization/ non synchronization of the chaotic system with the signal. For the time being, this novel approach to classification is limited to approximately periodic signals, such as they frequently occur in biology.

http://lanoswww.epfl.ch/staff/default.asp
Type of Seminar:
Public Seminar
Speaker:
Prof. Martin Hasler
EPF Lausanne Laboratory ofNon Linear Systems (DSC-Lanos) 1015- Ecublens
Date/Time:
Jan 16, 2002   17:15
Location:

ETH Zentrum, Gloriastrasse 35, 8006 Zürich, building ETZ, room E6
Contact Person:

Prof. M. Morari
No downloadable files available.
Biographical Sketch:
Martin Hasler received the Diploma in 1969 and the PhD degree in 1973 from the Swiss Federal Institute of Technology, Zurich, both in physics. He continued research in mathematical physics at Bedford College, University of London, from 1973 to 1974. At the end of 1974 he joined the Circuits and Systems group of the Swiss Federal Institute of Technology Lausanne (EPFL), where he was given the title of a Professor in 1984 and he became a full professor in 1996. During the 70’s, his research was concentrated on filter theory and design, in particular active and switched capacitor filters. Since 1980 his research is centered on nonlinear circuits and systems, including the qualitative analysis of resistive and dynamic circuits, the modeling and identification of nonlinear circuits and systems, neural networks and the engineering applications of complicated nonlinear dynamics, in particular chaos.