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The purpose of this paper is to present a new method that combines statistical techniques and neural networks in one method for the better time series prediction. In this paper we presented single exponential smoothing method (statistical technique) merged with feed forward back propagation neural network in one method named as smart single exponential(More)
In this paper we present our experimental results in data management for wireless sensor networks when an adapted Fuzzy ART model of neural networks is applied. Our system provides high dimension reduction and transfer savings, sending only classified identification number instead of whole sample. The system implementation is based on MicaZ sensor motes and(More)
Most of the problems for data management in today's wireless sensor networks were already dealt with during the past thirty years of the artificial neural-networks tradition and that kind of algorithms can be easily implemented to wireless sensor network platforms. These problems include the need for simple parallel distributed computation, possibility for(More)
Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and will meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. As a result of the dimensionality(More)
—With the increasing complexity of computing systems , complete hardware reliability can no longer be guaranteed. We need, however, to ensure overall system reliability. One of the most important features of artificial neural networks is their intrinsic fault-tolerance. The aim of this work is to investigate whether such networks have features that can be(More)
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