Abstract
A class of data-reusing (DR) learning algorithms for real-valued recurrent neural networks (RNNs) employed as nonlinear adaptive filters is extended to the complex domain to give a class of data-reusing learning algorithms for complex valued recurrent neural networks (CRNNs). For rigour, the derivation of the data-reusing complex real time recurrent learning (DRCRTRL) algorithm is undertaken for a general complex activation function. The analysis provides both error bounds and convergence conditions for the case of contractive and expansive complex activation functions. The improved performance of the data–reusing algorithm over the standard one is verified by simulations on prediction of complex valued signals.
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© 2003 Springer-Verlag Berlin Heidelberg
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Goh, S.L., Mandic, D.P. (2003). A Data-Reusing Gradient Descent Algorithm for Complex-Valued Recurrent Neural Networks. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_47
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DOI: https://doi.org/10.1007/978-3-540-45226-3_47
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