Feature trajectory reduction of integrated autoregressive processes based on a multilayer self-organizing neural network

Joerg Klose; Oliver Altena

doi:10.1117/12.49802

1 December 1991 Feature trajectory reduction of integrated autoregressive processes based on a multilayer self-organizing neural network

Joerg Klose, Oliver Altena

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Proceedings Volume 1565, Adaptive Signal Processing; (1991) https://doi.org/10.1117/12.49802
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

A method for reducing the parameters of time variant integrated autoregressive (IAR) processes by means of a multilayer self-organizing neural network is presented. For the test of signal detectors, observed time series are described by a time variant integrated AR-model. The characterizing parameter vector, called feature vector, is continuously calculated with every new observed time sample. The corresponding sequence of feature vectors results in the feature trajectory, which initializes an adaptive filter for generating new time series. We reduce feature trajectories by means of a multilayer self-organizing feature map. The presented network enables the mapping of each feature trajectory into a scalar sequence after optimizing the internal states of each map with an appropriate learning algorithm. Inverse mapping of each scalar sequence into a quantized trajectory shows good reproduction properties. The results show good tracking behavior and an acceptable data reduction which is asymptotically limited by the feature vector dimension. Experimental results are presented for time series corresponding to fire signals.

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Joerg Klose and Oliver Altena "Feature trajectory reduction of integrated autoregressive processes based on a multilayer self-organizing neural network", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); https://doi.org/10.1117/12.49802

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KEYWORDS

Signal processing

Stochastic processes

Signal detection

Error analysis

Neural networks

Matrices

Filtering (signal processing)

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