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Efficient online learning with Spiral Recurrent Neural Networks

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2 Author(s)
Rudolf Sollacher ; Siemens AG, Corporate Technology, Munich, Germany ; Huaien Gao

Distributed intelligent systems like self-organizing wireless sensor and actuator networks are supposed to work mostly autonomous even under changing environmental conditions. This requires robust and efficient self-learning capabilities implementable on embedded systems with limited memory and computational power. We present a new solution called spiral recurrent neural networks with an online learning based on an extended Kalman filter and gradients as in real-time recurrent learning. We illustrate its performance using artificial and real-life time series and compare it to other approaches. Finally we describe a few potential applications.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008