Abstract
The robust system identification method using the neural network is developed based on the canonical variate analysis (CVA). The main contribution of this algorithm is using CVA to obtain the k-step optimal prediction value. 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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Yamawaki, S., Jain, L. (2004). Robust System Identification Using Neural Networks. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_107
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DOI: https://doi.org/10.1007/978-3-540-30132-5_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
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