Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
394257 | Information Sciences | 2013 | 21 Pages |
Abstract
In this paper, a dynamic fuzzy inference marginal linearization (DFIML) method is proposed for modeling nonlinear dynamic systems. This method can transfer a group of input–output data into a time-variant fuzzy system with variable coefficients. It is shown that solutions of time-variant fuzzy systems generalized by DFIML method are universal approximators to solutions of a class of non-autonomous systems. Also the analytical solutions of these time-variant fuzzy systems can be obtained. Finally, a simulation example is provided to illustrate the validity and potential of the developed techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
De-Gang Wang, Wen-Yan Song, Peng Shi, Hong-Xing Li,