Piecewise linear controller improving its own reliability

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.