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Andrzej Cichocki, The Institute of Chemical and Physical Research (RIKEN) (Japan)
Ruck Thawonmas, The Institute of Chemical and Physical Research (RIKEN) (Japan)
Shun-ichi Amari, The Institute of Chemical and Physical Research (RIKEN) (Japan)
Two alternative neural-network methods are presented which both extract independent source signals one-by-one from a linear mixture of sources when the number of mixed signals is equal to or larger than the number of sources. Both methods exploit the previously extracted source signals as a priori knowledge so as to prevent the same signals from being extracted several times. One method employs a deflation technique which eliminates from the mixture the already extracted signals and another uses a hierarchical neural network which avoids duplicate extraction of source signals by inhibitory synapses between units. Extensive computer simulations confirm the validity and high performance of our methods.
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Uwe Marschner, Dresden University of Technology (Germany)
Wolf-Joachim Fischer, Dresden University of Technology (Germany)
In this paper the mapping of a nonlinear parameter estimator onto a DSP-architecture under use of an Electronic Design Automation (EDA) tool is presented. The work concentrates on the implementation of static nonlinear process models with SISO-structure and block processing. It is shown that the synthesized processor is able to perform such a parameter estimation in less than 30 us at a reasonable chip size.
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Ola Markusson, Royal Institute of Technology (KTH) (Sweden)
Torsten Bohlin, Royal Institute of Technology (KTH) (Sweden)
This contribution is a study of a method for identification of nonlinear stochastic models. Models generating electroencephalograms (EEG), based on neurophysiological knowledge axe studied, [1]. A model-based analysis of single evoked potentials is also suggested. The main idea behind the identification is to use an inverted model, since no general predictor is available for nonlinear models. A maximum likelihood (ML) method is used to estimate the structure and the parameters of the model. To utilize a priori knowledge a 'grey-box' approach is taken.
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