Modeling Uncertain Dynamic Plants With Interval Neural Networks by Bounded-Error Data

This paper presents a novel approach to building an interval dynamic model for an industrial plant with uncertainty by an interval neural network (INN). A new type of randomized learner model, named interval random vector functional-link network (IRVFLN), is proposed to take advantages of the inherent RVFLN in rapid modeling. The IRVFLN model is equipped with interval hidden input weights (and biases), which are randomly assigned from certain distribution/range and remain fixed, and the interval output weights can be evaluated by solving a couple of least squares problems. The comparative numerical experiments have verified the good potential of the proposed IRVFLN with the interval learner models produced by the error back-propagation algorithm. In the following modeling application, some measures for building IRVFLN with unknown but bounded (UBB) errors requirements are discussed in depth, in order to modeling an uncertain dynamic plant with IRVFLN by bounded-error data in either known or unknown error bounds. Finally, as a case study, the IRVFLN is applied to modeling a chemical interval dynamic plant with recycling, where the simulation results and generalization ability analysis demonstrate that the proposed method is suitable and effective.