Abstract
This paper addresses the problem of estimating training error bounds of state and output trajectories for a class of recurrent neural networks as models of nonlinear dynamic systems. We present training error bounds of trajectories between the recurrent neural network models and the target systems. The bounds are obtained provided that the models have been trained on N trajectories with N independent random initial values which are uniformly distributed over [a, b] m ε R m.
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© 1996 Springer-Verlag Berlin Heidelberg
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Liu, B., Si, J. (1996). Approximation errors of state and output trajectories using recurrent neural networks. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_135
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DOI: https://doi.org/10.1007/3-540-61510-5_135
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61510-1
Online ISBN: 978-3-540-68684-2
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