Using Feed Forward Neural Network to Solve Eigenvalue Problems

Tawfiq, L. N. M.; Salih, Othman Mahdi

doi:10.1155/2014/906376

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…where is arbitrary constant. Taking the new transform on both sides of Equation 23 There is many research study the convergence of methods such [18][19][20][21][22][23][24][25]. but here we introduce other manner illustrated in the following section.…”

Section: Examplementioning

confidence: 99%

New Technique for Solving Autonomous Equations

Enadi¹,

Tawfiq²

2019

IHJPAS

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This paper demonstrates a new technique based on a combined form of the new transform method with homotopy perturbation method to find the suitable accurate solution of autonomous Equations with initial condition. This technique is called the transform homotopy perturbation method (THPM). It can be used to solve the problems without resorting to the frequency domain.The implementation of the suggested method demonstrates the usefulness in finding exact solution for linear and nonlinear problems. The practical results show the efficiency and reliability of technique and easier implemented than HPM in finding exact solutions.Finally, all algorithms in this paper implemented in MATLAB version 7.12.

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Section: Examplementioning

confidence: 99%

New Technique for Solving Autonomous Equations

Enadi¹,

Tawfiq²

2019

IHJPAS

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“…Initialized neural background model by changing all model layers by estimated background model using temporal median method, and then each pixel of the current frame was compared to pixels of neural background model; if that pixel was close enough to estimated model in this case the pixel was estimated to be foreground pixel otherwise it was considered as background pixel. Finally, the model is updated in [13]. Rashid and Thomas, 2016, proposed an approach for simultaneous non-parametric modeling of foreground and background, which was applied in a competitive manner for pixels classification as foreground or background.…”

Section: Background Modelingmentioning

confidence: 99%

Modeling Dynamic Background based on Linear Equation

Rahma¹,

Basil²

2019

IHJPAS

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Detection moving car in front view is difficult operation because of the dynamic background due to the movement of moving car and the complex environment that surround the car, to solve that, this paper proposed new method based on linear equation to determine the region of interest by building more effective background model to deal with dynamic background scenes. This method exploited the permitted region between cars according to traffic law to determine the region (road) that in front the moving car which the moving cars move on. The experimental results show that the proposed method can define the region that represents the lane in front of moving car successfully with precision over 94%and detection rate 86% and FoM 90%.

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“…Therefore, it became necessary to focus on finding the best methods to solve it. There are many numerical and analytical methods used to solve partial differential equations, such as Admoain decomposition method [1 -6], homotopy perturbation method [7 -11], Laplace decomposition method [12,13], variational iteration method [14 -16], collocation method [17][18][19] and artificial neural network (Ann) [20 -24].…”

Section: Introductionmentioning

confidence: 99%

Modified New Iterative Method for Solving Nonlinear Partial Differential Equations

Jabber

2020

JAM

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In this paper, the iterative method, proposed by Gejji and Jafari in 2006, has been modified for solving nonlinear initial value problems. The Laplace transform was used in this modification to eliminate the linear differential operator in the differential equation. The convergence of the solution was discussed according to the modification proposed. To illustrate this modification some examples were presented.

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Using Feed Forward Neural Network to Solve Eigenvalue Problems

Cited by 3 publications

References 12 publications

New Technique for Solving Autonomous Equations

New Technique for Solving Autonomous Equations

Modeling Dynamic Background based on Linear Equation

Modified New Iterative Method for Solving Nonlinear Partial Differential Equations

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