Результаты поиска по 'simple regression':
Найдено статей: 2
  1. Khudhur H.M., Halil I.H.
    Noise removal from images using the proposed three-term conjugate gradient algorithm
    Computer Research and Modeling, 2024, v. 16, no. 4, pp. 841-853

    Conjugate gradient algorithms represent an important class of unconstrained optimization algorithms with strong local and global convergence properties and simple memory requirements. These algorithms have advantages that place them between the steep regression method and Newton’s algorithm because they require calculating the first derivatives only and do not require calculating and storing the second derivatives that Newton’s algorithm needs. They are also faster than the steep descent algorithm, meaning that they have overcome the slow convergence of this algorithm, and it does not need to calculate the Hessian matrix or any of its approximations, so it is widely used in optimization applications. This study proposes a novel method for image restoration by fusing the convex combination method with the hybrid (CG) method to create a hybrid three-term (CG) algorithm. Combining the features of both the Fletcher and Revees (FR) conjugate parameter and the hybrid Fletcher and Revees (FR), we get the search direction conjugate parameter. The search direction is the result of concatenating the gradient direction, the previous search direction, and the gradient from the previous iteration. We have shown that the new algorithm possesses the properties of global convergence and descent when using an inexact search line, relying on the standard Wolfe conditions, and using some assumptions. To guarantee the effectiveness of the suggested algorithm and processing image restoration problems. The numerical results of the new algorithm show high efficiency and accuracy in image restoration and speed of convergence when used in image restoration problems compared to Fletcher and Revees (FR) and three-term Fletcher and Revees (TTFR).

    Keywords: nonsmooth, restoration, globally, descent, numerical, optimization.
  2. Mikheev A.V., Kazakov B.N.
    A New Method For Point Estimating Parameters Of Simple Regression
    Computer Research and Modeling, 2014, v. 6, no. 1, pp. 57-77

    A new method is described for finding parameters of univariate regression model: the greatest cosine method. Implementation of the method involves division of regression model parameters into two groups. The first group of parameters responsible for the angle between the experimental data vector and the regression model vector are defined by the maximum of the cosine of the angle between these vectors. The second group includes the scale factor. It is determined by means of “straightening” the relationship between the experimental data vector and the regression model vector. The interrelation of the greatest cosine method with the method of least squares is examined. Efficiency of the method is illustrated by examples.

    Keywords: simple regression, point estimation, method of least squares, two-exponential luminescence decay, boiling point of water, electrical resistivity, Bloch–Gruneisen function.
    Views (last year): 2. Citations: 4 (RSCI).

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