Mohammad Jahani Moghaddam - Academia.edu (original) (raw)
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Papers by Mohammad Jahani Moghaddam
Mathematical Methods in the Applied Sciences, 2018
This paper presents a new multiple-input-single-output nonlinear system identification method bas... more This paper presents a new multiple-input-single-output nonlinear system identification method based on Hammerstein model, which includes a fractional transfer function and a Modified Radial Basis Function Neural Network (MRBFNN) as linear dynamic part and static nonlinear subsystem, respectively. The size of Radial Basis Function Neural Network (RBFNN) grows with the number of inputs exponentially. As a novel idea, the MRBFNN is proposed, whose adjustable parameters are far fewer than other RBFNNs presented yet. A Modified Genetic Algorithm is used to identify the fractional orders and the centers and widths of MRBFNN and obtain an initial estimation of other unknown parameters. The Recursive Least Square (RLS) method is used to improve the estimation by updating the weighting parameters of MRBFNN and the transfer function coefficients. The convergence analysis of the proposed RLS is provided. Simulation results show the effectiveness and accuracy of the proposed method.
2010 2nd International Conference on Computer Engineering and Technology, 2010
Modeling of special kind of ultrasonic motor i.e. Traveling Wave Ultrasonic Motor (TWUSM) by mean... more Modeling of special kind of ultrasonic motor i.e. Traveling Wave Ultrasonic Motor (TWUSM) by means of genetic algorithm (GA) & neural network (NN) based Hammerstein model and its control by GA-NN based Model Predictive Control (MPC) is presented in this paper, in which the nonlinear static part of model is approximated by a GAbased radial basis function neural network (RBFNN) and the linear dynamic part is modeled by experimental measurement. GA is also adopted to optimize the hidden centers, the radial basis function widths and the weights of the RBFNN. A nonlinear MPC based on the Hammerstein model is developed to obtain precise USM position control. The simulation results show that the proposed approach is very effective, suitable and useful for control of TWUSM.
Applied Soft Computing, 2018
This paper proposes a two stage method for identification of multiple-input single-output Hamme... more This paper proposes a two stage method for identification of multiple-input single-output Hammerstein model with time delay.
Mathematical Methods in the Applied Sciences, 2018
This paper presents a new multiple-input-single-output nonlinear system identification method bas... more This paper presents a new multiple-input-single-output nonlinear system identification method based on Hammerstein model, which includes a fractional transfer function and a Modified Radial Basis Function Neural Network (MRBFNN) as linear dynamic part and static nonlinear subsystem, respectively. The size of Radial Basis Function Neural Network (RBFNN) grows with the number of inputs exponentially. As a novel idea, the MRBFNN is proposed, whose adjustable parameters are far fewer than other RBFNNs presented yet. A Modified Genetic Algorithm is used to identify the fractional orders and the centers and widths of MRBFNN and obtain an initial estimation of other unknown parameters. The Recursive Least Square (RLS) method is used to improve the estimation by updating the weighting parameters of MRBFNN and the transfer function coefficients. The convergence analysis of the proposed RLS is provided. Simulation results show the effectiveness and accuracy of the proposed method.
2010 2nd International Conference on Computer Engineering and Technology, 2010
Modeling of special kind of ultrasonic motor i.e. Traveling Wave Ultrasonic Motor (TWUSM) by mean... more Modeling of special kind of ultrasonic motor i.e. Traveling Wave Ultrasonic Motor (TWUSM) by means of genetic algorithm (GA) & neural network (NN) based Hammerstein model and its control by GA-NN based Model Predictive Control (MPC) is presented in this paper, in which the nonlinear static part of model is approximated by a GAbased radial basis function neural network (RBFNN) and the linear dynamic part is modeled by experimental measurement. GA is also adopted to optimize the hidden centers, the radial basis function widths and the weights of the RBFNN. A nonlinear MPC based on the Hammerstein model is developed to obtain precise USM position control. The simulation results show that the proposed approach is very effective, suitable and useful for control of TWUSM.
Applied Soft Computing, 2018
This paper proposes a two stage method for identification of multiple-input single-output Hamme... more This paper proposes a two stage method for identification of multiple-input single-output Hammerstein model with time delay.