Abstract
In the past few years, there has been increased interest in the modeling and control of non-linear systems using Neural Networks (NNs) [1–12]. Applications of NNs published in literature dealt with I/O mappings. Recently, however, there has been increased interest in Input — state — Output mapping representation using Dynamic Recurrent Neural Networks (DRNNs) [13–16]. DRNNs are Feed Forward Neural Networks (FFNNs) [17,18] with feedback connections, which enable the description of temporal behavior to be stored in the neurons, allowing the NNs to account for the nonlinear dynamics. Funahashi and Nakamura [19] have shown that finite time trajectories of an n dimensional system can be approximated by the states of a Hopfield network, with n output nodes, N hidden nodes, and appropriate initial states.
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Kárný, M., Warwick, K., Kůrková, V. (1998). A Priori Information in Network Design. In: Kárný, M., Warwick, K., Kůrková, V. (eds) Dealing with Complexity. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1523-6_7
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DOI: https://doi.org/10.1007/978-1-4471-1523-6_7
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