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Linear-wavelet networks

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This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.
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Suboptimal fault tolerant control design with the use of discrete optimization

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This paper presents a concept of designing fault tolerant control systems with the use of suboptimal methods. We assume that a given (nonlinear) dynamical process is described in a state space. The method consists in searching (at the off-line stage) for a trajectory of operational points of the system state space. The sought trajectory can be constrained by certain conditions, which can express faults or failures already detected. Within this approach, we are able to use the autonomous dynamics of the process in order to minimize a control cost index (a sub-optimality property). The search itself is based on finding a cheapest path in a graph structure, which represents the system's dynamics described in the state space. Such a cheapest path (if it exists) represents the sought trajectory. Another (on-line) design stage consists in tracking this trajectory by an executive controller.
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