A system identification using DRNN based on swarm intelligence

Authors:
Qunzhou Yu;Jian Guo;Cheng Zhou
Affiliations:
School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China;Department of Controlled Science and Engineering, Huazhong University of Science and Technology, Wuhan, China;School of Civil Engineering and Mechanic, Huazhong University of Science and Technology, Wuhan, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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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.