Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
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ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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This paper proposes a predictive self-organizing map (P-SOM) that performs an adaptive vector quantization of migratory time-sequential signals whose stochastic properties such as average values of signals in each cluster are varying continuously. The P-SOM possesses not only the weight corresponding to the signal values themselves but also those related to the time-derivative information. All the weights self-organize to predict appropriate future reference vectors. The prediction using the time-derivative weights enables the separation of continuously varying components form random noise components, resulting in a better performance of the adaptive vector quantization. That is to say, the stationary random noise components are captured by the ordinary weights, whereas the migrating components are captured by the first (and higher) order time-derivative ones. An application to a mobile communication receiver using quasi-coherent detection is presented. By utilizing both the ordinary and time-derivative weights consistently, the P-SOM generates a predictive reference vectors and quantizes the migratory signals adaptively. Simulation experiments on the bit-error rates (BERs) demonstrate that a P-SOM adaptive demodulator has a superior capability to track phase rotations caused by the Doppler effect. A theoretical noise analysis is also reported for the conventional SOM and the P-SOM. It is found that the calculation results are approximately in good agreement with the experimental ones.