Abstract
Original Elman, which is one of the well-known dynamic recurrent neural network (DRNN), has been improved to easily apply in dynamic systems identification during the past decade. In this paper, a learning algorithm for Original Elman neural networks (ENN) based on modified particle swarm optimization (MPSO), which is a swarm intelligent algorithm (SIA), is presented. MPSO and Elman are hybridized to form MPSO-ENN hybrid algorithm as a system identifier. Simulation experiments show that MPSO-ENN is a more effective swarm intelligent hybrid algorithm (SIHA), which results in an identifier with the best trained model. Dynamic identification system (DIS) of the MPSO-ENN is obtained.
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Yu, Q., Guo, J., Zhou, C. (2010). A System Identification Using DRNN Based on Swarm Intelligence. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_18
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DOI: https://doi.org/10.1007/978-3-642-13498-2_18
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