Please use this identifier to cite or link to this item:
Title: Extended self-organizing map for nonlinear system identification
Authors: Ge, Ming
Chiu, Min-Sen 
Wang, Qing-Guo 
Issue Date: 1999
Source: Ge, Ming,Chiu, Min-Sen,Wang, Qing-Guo (1999). Extended self-organizing map for nonlinear system identification. Proceedings of the IEEE Conference on Decision and Control 1 : 1065-1070. ScholarBank@NUS Repository.
Abstract: Local model networks (LMN) are employed to represent a nonlinear dynamical system with a set of locally valid sub-models across the operating range. A new extended self-organizing map network (ESOM) is developed in this paper for the identification of the LMN. The ESOM is a multi-layered network that integrates the basic elements of traditional self-organizing maps and a feed-forward network into a connectionist structure which distribute the learning tasks. A novel two-phase learning algorithm is introduced for constructing the ESOM from plant input-output data, with which the structure is determined through the self-organizing and the parameters are obtained with the linear least square optimization method. The predictive performance of the model derived from the ESOM is evaluated in three case studies. Simulation results demonstrate the effectiveness of the proposed scheme in comparison with other methods.
Source Title: Proceedings of the IEEE Conference on Decision and Control
ISSN: 01912216
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 13, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.