Research on System Identification Based on Modeling Accuracy

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Generally speaking, identification error should be minimized in system identification. The error comes from several sources, such as observation error and computation error. In this research, such identification errors have been discussed and a novel identification approach has been considered and some applications of identification have been discussed..1. Analysis of identification errorsComputation error in system identification has been discussed using the idea of interval arithmetic. Based on the analysis, an identification approach has been developed, of which applicability will be considered in the future.2. Recursive estimation based on stochastic approximation Stochastic approximation method to estimate the time-varying root of the regression function has been discussed. To improve its convergence, a transformation function for residuals is constructed using the empirical distribution of the residuals. The idea of random varying truncations is applied to relax the convergence conditions. These ideas are applied to design nonlinear filters.3. Change detection in systems with uncertaintyChange detection has been discussed for the systems with model uncertainty, which is caused by identification error.