Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407725 | Neurocomputing | 2015 | 10 Pages |
Abstract
In this paper, we utilize generalized Bernstein polynomials to construct fuzzy system. Different from traditional Bernstein polynomials, partition of interval on input variable can be chosen as non-equidistant division. We prove that generalized Bernstein fuzzy systems are universal approximators to a given continuous function and its high-order derivatives. Further, ELM method is used to tune the parameters of generalized Bernstein fuzzy system and Spline fuzzy system. It is proved that ELM-Spline fuzzy system can approximate a function and its derivative. Simulation examples show that the proposed ELM-Bernstein fuzzy system and ELM-spline fuzzy system can achieve high approximation capability for nonlinear model.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
De-Gang Wang, Wen-Yan Song, Hong-Xing Li,