Piecewise linear controller improving its own reliability

doi:10.1016/0959-1524(95)00049-6

Return

Piecewise linear controller improving its own reliability

1996-04-01

PRE

Yoshihiro Hashimoto *

DOI：10.1016/0959-1524(95)00049-6

Original

Original request for help

Save

Abstract

En 中文

Although the capability of neural networks in nonlinear dynamics modelling is well-established, the reliability of the output heavily depends on the training data. The reliability is a serious problem in applying it to real problems. in this paper, we propose a radial basis functions network (RBFN) which evaluates its own reliability and improves itself recursively. This network approximates the input-output relationships with a piecewise linear regression. An adaptive internal model control algorithm in which the reliability of the model is used to tune the controller performance, is also proposed.

Keywords:

neural network

piecewise linear regression

nonlinear control

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.