new paper on "Analysis of Switching Dynamical Systems"

[klaus@prosun.first.gmd.de]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/pawelzik.switch.ps.Z 
FTP-file: pub/neuroprose/mueller.switch_speech.ps.Z


The following 2 papers are now available for copying from the Neuroprose 
repository: pawelzik.switch.ps.Z, mueller.switch_speech.ps.Z 

pawelzik.switch.ps.Z 
(124459 bytes) 16 pages. 

Pawelzik, K.,  Kohlmorgen, J., M\"uller, K.-R.:
Annealed Competition of Experts for a Segmentation and Classification of 
Switching Dynamics

We present a method for the unsupervised segmentation of data streams 
originating from different unknown sources which alternate in time. 
We use an architecture consisting of competing neural networks. 
Memory is included in order to resolve ambiguities of input-output relations. 
In order to obtain maximal specialization, the competition is adiabatically 
increased during training. 
Our method achieves almost perfect identification and segmentation in the
case of switching chaotic dynamics where input manifolds overlap and 
input-output relations are ambiguous. 
Only a small dataset is needed for the training proceedure.
Applications to time series from complex systems demonstrate the potential
relevance of our approach for time series analysis and short-term prediction.

(Neural Computation in Press). 

mueller.switch_speech.ps.Z 
(427948 bytes) 11 pages. 

M\"uller, K.-R., Kohlmorgen, J., Pawelzik, K.: 
Analysis of Switching Dynamics with Competing Neural Networks, 

We present a framework for the unsupervised segmentation of time series.
It applies to non-stationary signals originating from different dynamical 
systems which alternate in time, a phenomenon which appears in many
natural systems.   
In our approach, predictors compete for data points of a given time series. 
We combine competition and evolutionary inertia to a learning rule. 
Under this learning rule the system evolves such that the predictors, which 
finally survive, unambiguously identify the underlying processes.
Applications to time series from complex systems and speech are presented.
The segmentation achieved is very precise and transients are included,
a fact, which makes our approach promising for several applications. 

(IEICE Transactions on Fundamentals of Electronics, Communications and 
Computer Sciences in Press).


* NO HARDCOPIES *


Best regards,

Klaus



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Dr. Klaus-Robert M\"uller
C/o Prof. Dr. S. Amari
Department of Mathematical Engineering
University of Tokyo
7-3-1 Hongo, Bunkyo-ku
Tokyo 113 , Japan

mail: klaus at sat.t.u-tokyo.ac.jp
Fax:   +81 - 3 - 5689 5752

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PERMANENT ADRESS:
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Dr. Klaus-Robert M\"uller
GMD First (Gesellschaft f. Mathematik und Datenverarbeitung)
Rudower Chaussee 5, 12489 Berlin
Germany

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