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Autonomous, self* sensor networks require sensor nodes with a certain degree of " intelligence ". An elementary component of such an " intelligence " is the ability to learn online predicting sensor values. We consider recurrent neural network (RNN) models trained with an extended Kalman filter algorithm based on real time recurrent learning (RTRL) with(More)
Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neu-ral networks implies storing them as different attractors of the recurrent hidden layer dynamics. A challenging test and also useful for application is conditional prediction of sequences giving just the trigger signal as an(More)
This article deals with the modeling and management of spatiotemporal systems using machine learning and self-organization algorithms. Two application examples are the localization of objects from radio measurements using spatiotemporal models learned from data, and the self-organizing management of wireless multi-hop sensor networks. For both examples we(More)
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(More)
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