title: Topology preservation and cooperative learning in identification of multiple model systems.
abstract: The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to formally evaluate the effects of cooperative learning and topology preservation in identification of multiple model system based on SON. The results of the mathematical analysis supports the heuristic that a good learning strategy for identifying the local model parameters of a SON-based multiple model system is to choose a neighborhood function whose effective region is initially wider and is reduced gradually during learning. An example of nonlinear function approximation is also provided at the end of this paper to demonstrate the results of the mathematical analysis.
authors.affiliation: Manufacturing Systems Research Laboratory, General Motors Research and Development, Warren, MI 48090-9055, USA. jianbo.liu@gm.com
authors.forename: Jianbo
authors.initials: J
authors.lastname: Liu
authors.forename: Dragan
authors.initials: D
authors.lastname: Djurdjanovic
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*