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MONOTONE ITERATION SCHEME FOR A FORCED DUFFING EQUATION WITH NONLOCAL THREE-POINT CONDITIONS
Alsaedi, Ahmed Korean Mathematical Society 2007 대한수학회논문집 Vol.22 No.1
In this paper, we apply the generalized quasilinearization technique to a forced Duffing equation with three-point mixed nonlinear nonlocal boundary conditions and obtain sequences of upper and lower solutions converging monotonically and quadratically to the unique solution of the problem.
Ahmad, Bashir,Alsaedi, Ahmed,Garout, Doa'a Korean Mathematical Society 2008 대한수학회지 Vol.45 No.5
In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem.
Bashir Ahmad,Ahmed Alsaedi,Doa'A Garout 대한수학회 2008 대한수학회지 Vol.45 No.5
In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem. In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem.
Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems
Xian Lu,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.8
This paper concerns the parameter identification methods of multivariable equation-error systems. By means of the decomposition technique, the multivariable identification model is transformed into two subidentification models and a decomposition-based stochastic gradient (D-SG) algorithm is presented for estimating the parameters of these two submodels. In order to further improve the convergence rate and the parameter estimation accuracy, we expand the innovation vectors to the innovation matrices and develop a decomposition-based multi-innovation stochastic gradient (D-MISG) algorithm. The simulation results confirm that the D-MISG algorithm can provide more accurate parameter estimates than the D-SG algorithm.
Mengting Chen,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.6
This paper focuses on the identification problem for a class of bilinear-in-parameter systems with an additive noise modeled by an autoregressive moving average process. By using the over-parameterization model, the special form of the bilinear term can be obtained by the model equivalent transformation. Then, we use a decomposition of the model into two synthetic models in order to separate the effect of the two sets of parameters, i.e., the coefficients of the nonlinear basis functions from the parameters of the colored noise. Moreover, two decomposition based iterative algorithms are proposed to identify the unknown parameters. A numerical example is presented to confirm the effectiveness of the proposed methods.
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
Ling Xu,Weili Xiong,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
This paper studies the problem of parameter estimation for frequency response signals. For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal. When a multifrequency sine signal is applied to a system, the system response also is a multi-frequency sine signal. The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output. In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data. By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition. Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm. The simulation results show the effectiveness of the proposed methods.
Recursive Identification Algorithms for a Class of Linear Closed-loop Systems
Huan Xu,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.12
This paper focuses on the identification problems for a class of linear closed-loop systems. On one hand, the identifiability condition is investigated for the case where the controller is in series with the plant on the forward channel. On the other hand, the identification model is derived after parametrization, in which the parameter vector only contains the parameters of the controlled plant instead of the whole closed-loop system, and a recursive least squares algorithm and a stochastic gradient algorithm are proposed for closed-loop systems. In order to improve the parameter estimation accuracy, a forgetting factor and a convergence index are introduced into the proposed stochastic gradient algorithm. The simulation results demonstrate the effectiveness of the proposed algorithms.
Qinyao Liu,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.3
This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.
Yihong Zhou,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.11
This paper studies some parameter estimation algorithms for a class of nonlinear models with exponential terms, i.e., the radial basis function-based state-dependent autoregressive (RBF-AR) models. An Aitken-based multi-innovation stochastic gradient algorithm is presented for the RBF-AR models based on the Aitken method. Inspired by the decomposition-coordination principle of large systems, an Aitken-based hierarchical multi-innovation stochastic gradient algorithm is proposed by combining the decomposition technique with the Aitken method. The effectiveness of the proposed algorithms are validated through two simulation examples.
Alsulami, Fairouz,Alseleahbi, Hind,Alsaedi, Rawan,Almaghdawi, Rasha,Alafif, Tarik,Ikram, Mohammad,Zong, Weiwei,Alzahrani, Yahya,Bawazeer, Ahmed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9
Glaucoma is a chronic neuropathy that affects the optic nerve which can lead to blindness. The detection and prediction of glaucoma become possible using deep neural networks. However, the detection performance relies on the availability of a large number of data. Therefore, we propose different frameworks, including a hybrid of a generative adversarial network and a convolutional neural network to automate and increase the performance of glaucoma detection. The proposed frameworks are evaluated using five public glaucoma datasets. The framework which uses a Deconvolutional Generative Adversarial Network (DCGAN) and a DenseNet pre-trained model achieves 99.6%, 99.08%, 99.4%, 98.69%, and 92.95% of classification accuracy on RIMONE, Drishti-GS, ACRIMA, ORIGA-light, and HRF datasets respectively. Based on the experimental results and evaluation, the proposed framework closely competes with the state-of-the-art methods using the five public glaucoma datasets without requiring any manually preprocessing step.