The design of closed-loop structural control systems necessitates a certain level of robustness to cope with system uncertainties. Neurocontrollers, a type of adaptive control system, have been proposed to cope with those uncertainties. However, the performance of neural networks can be substantially influenced by the choice of the input space, or the hyperspace in which the representation lies...

application of automatic systems, including discharge measurement technology is one of the effective methods for improving delivery management, water use efficiency, flexibility, and performance of irrigation networks. several aspects such as hydraulic, technical, physical, environmental, economic, social, and managerial, have significant impact on these systems selection. due to diversity of t...

An iterative backward selection method for determination of relevant input variables in data-driven fuzzy modeling is presented. The method utilizes parameters of the Takagi-Sugeno model as a factor to determine the significance of input variables. As a result, it is less computationally intensive than most of the existing methods for input variable selection.

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

The accuracy of an identified model depends on the choice of input signal. Persistency of excitation is a necessary criterion for such signals. In this paper we develop additional criteria for input signal selection, in particular, the input at each time step is chosen to minimize the predicted variance of the system estimate at the next time step. We extend the method to the finitehorizon inpu...

We propose the threshold updating method for terminating variable selection and two variable selection methods. In the threshold updating method, we update the threshold value when the approximation error smaller than the current threshold value is obtained. The first variable selection method is the combination of forward selection by block addition and backward selection by block deletion. In...