An unrestricted algorithm for accurate prediction of multiple-input multiple-output (MIMO) Wiener processes

Previous research [N. Bhandari and D. K. Rollins, Ind. Eng. Chem. Res., 2003, 42, 5583] introduced a methodology for obtaining accurate continuous-time multiple-input, multiple-output (MIMO) models for Wiener processes with nonlinear static and dynamic behavior. This methodology consists of a model-building procedure for estimation of model forms in the Wiener structure and a choice of two algorithms for exact predictions of true Wiener systems. One algorithm uses only the most-recent input changes but is restricted to approximately steady-state conditions between input changes. The other algorithm has no restricted conditions but is dependent on all past input changes and, thus, requires a fading memory treatment. This article extends the former algorithm by proposing a new continuous-time algorithm that is not restricted by steady-state conditions between input changes. In addition, the proposed algorithm is dependent only on the most-recent input changes. Evaluation of the proposed algorithm is conducted using a simulated continuously stirred tank reactor (CSTR) that closely follows a Wiener process; the results of this study are compared with the other two previously mentioned algorithms. Results are given for two basic cases: (i) no noise and (ii) independently, identically, and normally distributed noise.