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In this paper, we present a new learning algorithm of neural network based on the orthogonal decomposition method (ORT). The main scheme of this algorithm is using the ORT to obtain specially structured subspaces defined by the input-output data. This structure is then exploited in the calculation of the parameter estimation of the neural network. Therefore, the method to obtain the comparatively accurate estimate is introduced without iteration calculations. We show that this algorithm can be applied to successfully identify the nonlinear system in the presence of comparatively loud noise. Results from several simulation studies have been included to the effectiveness of this method.
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