Maryam Salimifard

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This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant(More)
Nonlinear multi-input multi-output (MIMO) models seem quite suitable to represent most industrial systems and many control problems. Besides, the outputs of the real systems are usually correlated with noises which might not satisfy the assumption of white noises. This paper proposes an efficient identification method for a class of nonlinear MIMO systems(More)
Though Model Predictive Control (MPC) is among the promising methods for teleoperation system applications, its time consuming computations is considered as a major limitation. Intelligent algorithms seem to be powerful tools to overcome such issues. Prediction of the process outputs by using a model and solving one constrained linear or nonlinear(More)
Most of the real industrial systems are nonlinear and multivariable which might be correlated with some noises. Therefore, considering a model which can effectively characterize these types of systems are very appealing. In this regard, this paper presents a multivariable Hammerstein­ Wiener model for identification of nonlinear systems with moving average(More)
Since orthogonal representations of functions are among the most desirable and efficient approximation schemes, this paper proposes an appropriate combination of two classes of orthonormal basis functions for nonlinear dynamic system identification. For this purpose, the nonlinear Wiener model is studied which consists of a linear time invariant (LTI)(More)
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