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The forecasting of hydrological variables, such as streamflow, plays an important role in water resource planning and management. Recently, the development of stochastic models is regarded as a major step for this purpose. Streamflow forecasting using the ARIMA model can be conducted when unknown parameters are estimated correctly because parameter estimation is one of the crucial steps in modeling process. The main objective of this research is to explore the performance of parameter estimation methods in the ARIMA model. In this study, four parameter estimation methods have been used: (i) autocorrelation function based on model parameters; (ii) conditional likelihood; (iii) unconditional likelihood; and (iv) genetic algorithm. Streamflow data of Ouromieh River basin situated in Northwest Iran has been selected as a case study for this research. The results of these four parameter estimation methods have been compared using RMSE, RME, SE, MAE and minimizing the sum squares of error. This research indicates that the genetic algorithm and unconditional likelihood methods are, respectively, more appropriate in comparison with other methods but, due to the complexity of the model, genetic algorithm has high convergence to a global optimum.

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In this paper, mass flow rate and location of leakage in natural gas pipeline has been estimated simultaneously using inverse analysis. For doing so, at first natural gas transient flow in pipeline has been simulated numerically; this simulation is named direct problem. In the direct problem, it is assumed that the mass flow rate and location of leakage is definite and the governing equations are inhomogeneous well-known Euler equations. In these equations, the leakage effect has been considered as a source term. Steger–Warming flux splitting method has been used for numerical analysis of these equations. Then the location and mass flow rate of gas leakage of pipeline have been estimated simultaneously using Levenberg-Marquardt method for parameter estimation. This method is an iterative algorithm and based on minimizing the sum of the squares of the errors which are difference between pressures computed by the direct problem and pressures measured by pressure gauges. The results of the direct problem have good agreement with Mac–Cormack method and characteristics method of specified time intervals. The results of the inverse analysis demonstrate that Levenberg-Marquardt algorithm is stable and efficient enough to estimate simultaneously the mass flow rate and location of leakage in natural gas pipeline.

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The Unscented Kalman filter (UKF) is the popular approach to estimate the recursive parameter of nonlinear dynamical system corrupted with Gaussian and white noises. Also, it has been applied to train the weights of the multi-layered neural network (MNN) models. The Group method of data handling (GMDH)-type neural network is one of the most widely used neural networks which has high capacity in modeling of the complex data. In many researches, different approaches are used in training of neural networks in terms of associated weights or coefficients, such as singular value decomposition, and genetic algorithms. In this paper, the unscented Kalman filter is used to train the parameters of GMDH-type neural network when the experimental data are deterministic. The effectiveness of GMDH-type neural network with UKF algorithm is demonstrated by the modeling of the using a table of the multi input-single output experimental data. The simulation result shows that the UKF-based GMDH algorithm perform well in modeling of nonlinear systems in comparison with the results of using traditional GMDH-type neural network and is more robust against the model and measurement uncertainty.

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In this paper, a trajectory tracking control strategy for a quadrotor flying robot is developed. At first, dynamic model is obtained by lagrange-euler approach. Then, control structure, consisting of a model-based predictive controller, has been used based on state space error to track transitional movements for reference trajectory and also robust nonlinear H∞ control is applied for stabilizing the rotational movements and reject the external disturbance. In both controllers the integral of the position error is considered, allowing the achievement of a null steady-state error when sustained disturbances are acting on the system. The external disturbances is considered as aerodynamic torques. If uncertainties increase, the designed control system will be unable to track and stabilizing perform properly and completely. So finally, in order to eliminate the effects of parameter uncertainties the recursive least squares is used for estimating mass and moment inertia parameters which are linear and it is applied to the control system. Simulation results show that by using estimation of system parameters, the proposed control system has a promising performance in terms of stabilization and position tracking even in the presence of external disturbance and parametric uncertainties.

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Cell injection system in medicine used to inject the materials into the cells. The injection system consists of Injector and rotating plate. The controller sets height, position and orientation of the rotating plate. The proposal of this article is to replace SCARA robot injection tool and it included ability in desired position tracking and applied to time-varying force. In recent articles the control system applies to the rotating plate of Cells and this method can cause the damaging risk. The proposed method is fixed plate and to increase the success rate, the robot had been controlled. The parameters of environmental models are estimated by nonlinear proposed models and by using the recursive method, the minimum of squares errors will be optimal. The voltage strategy can control robot actuators. This method is simpler and free from the manipulator dynamics. In all recent studies, the impedance control is based on the torque control method and the proposed method of this article is applying the impedance control using voltage control. The robust adaptive impedance controller is designed in the presence of uncertainties. The simulation's results demonstrate desired performance of the proposed method

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In this paper, the diffusion coefficient in a normal tissue and tumor are to be estimated by the method of inverse problems. At the beginning, distribution of drug (with the assumption of uniform and isentropic diffusion coefficient) in the tissue is considered as the direct problem. In the direct problem, the governing equation is the convection–diffusion, which is the generalized form of fick’s law. Here, a source and a sink are defined; the source as the rate of solute transport per unit volume from blood vessels into the interstitial space and the sink as the rate of solute transport per unit volume from the interstitial space into lymph vessels are added to this equation. To solve the direct problem, the finite difference method has been considered. Additionally, the diffusion coefficient of a normal tissue and tumor will be approximated by parameter estimation method of Levenberg-Marquardt. This method is based on minimizing the sum of squared errors which in the present study, considered error is the difference of the estimated concentration and the concentration measured by medical images (simulated numerically). Finally, the results obtained by Levenberg-Marquardt method have provided an acceptable estimation of diffusion coefficient in normal tissue and tumor.

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In this paper, a numerical algorithm based inverse method is used to estimate effective diffusion coefficient by using experimental tracer distribution. The Algorithm uses factitious experimental data which are produced by adding noise to numerical data obtained from direct problem. A comprehensive model (Diffusion-Convection-Reaction) is used to derive PET tracer distribution in tumor tissue with microvasculature network. This model was used because of considering all transport phenomena in tissue. In this work to achieve accurate distribution of tracer in tumor tissue, convection diffusion reaction equation which is a PDE is implemented. The proposed tracer in this work is Fluorodeoxyglucose (18F). Solution of inverse problem for estimating effective Diffusion Coefficient is based on minimization of least squares norm. In this work Levenberg-Marquardt technique is applied. Solution of parameter estimation problem require calculation of sensitivity matrix which elements are sensitivity coefficients. Sensitivity coefficients shows differentiation of Tracer concentration with respect to Effective Diffusion coefficient variation is calculated using first derivation of concentration equation. The equations of concentration distribution and sensitivity coefficients are solved using Finite volume method. The results show that the numerical algorithm is able to estimate the effective diffusion coefficient in tissue.