K. Pawelzik et al., ANNEALED COMPETITION OF EXPERTS FOR A SEGMENTATION AND CLASSIFICATIONOF SWITCHING DYNAMICS, Neural computation, 8(2), 1996, pp. 340-356
We present a method for the unsupervised segmentation of data streams
originating from different unknown sources that alternate in time. We
use an architecture consisting of competing neural networks. Memory is
included to resolve ambiguities of input-output relations. To obtain
maximal specialization, the competition is adiabatically increased dur
ing training. Our method achieves almost perfect identification and se
gmentation in the case of switching chaotic dynamics where input manif
olds overlap and input-output relations are ambiguous. Only a small da
taset is needed for the training procedure. Applications to time serie
s from complex systems demonstrate the potential relevance of our appr
oach for time series analysis and short-term prediction.