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This paper presents a new type of two-stage fuzzy generalized assignment problem (FGAP) models with critical value-at-risk (VaR) criteria based on credibility theory and two-stage fuzzy optimization method. Since the proposed FGAP model often includes fuzzy coefficients defined through known possibility distributions, it is inherently an infinite-dimensional optimization problem that can rarely be...
In this technical note, we consider adaptive control of single input uncertain nonlinear systems in the presence of input saturation and unknown external disturbance. By using backstepping approaches, two new robust adaptive control algorithms are developed by introducing a well defined smooth function and using a Nussbaum function. The Nussbaum function is introduced to compensate for the nonlinear...
In this paper, an adaptive controller is developed for uncertain nonlinear systems in the presence of input saturation. The control design is achieved by using backstepping technique with neural network approximation. Unlike some existing control schemes for systems with input saturation, the developed controller does not require uncertain parameters within a known compact set. Besides showing stability,...
This paper reconstructs multivariate functions from scattered data by a new multiscale technique. The reconstruction uses support vector regression model by positive definite reproducing kernels in Hilbert spaces. But it adopts techniques from wavelet theory and shift-invariant spaces to construct a new class of kernels as multiscale superpositions of shifts and scales of a single compactly supported...
This paper presents a robust fault detection (FD) scheme for detecting and approximating state faults occurring in a class of nonlinear dynamical systems. In the presence of a failure, the values exported by the on-line approximator (OLA), are used as an estimate of the real nonlinear fault function. The general inspiration for constructing OLA model in FD is based on the radial basis function (RBF)...
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