title: Flexible structure multiple modeling using irregular self-organizing maps neural network.
abstract: The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.
authors.affiliation: Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran. fatehi@kntu.ac.ir
authors.forename: Alireza
authors.initials: A
authors.lastname: Fatehi
authors.forename: Kenichi
authors.initials: K
authors.lastname: Abe
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008377:Maps as Topic; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated