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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.