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Activation Functions Effect on Fractal Coding Using Neural Networks

Intelligent Automation & Soft Computing 2023年第4期36卷 957-965页

作者： rashad a.al-jawfi Department of Mathematics Faculty of Sciences and ArtsNajran UniversityKSA

Activation functions play an essential role in converting the output of the artiﬁcial neural network into nonlinear results,since without this nonlinearity,the results of the network will be less *** is the mission of all nonlinear functions,except for *** activation function must be dif-ferentiable for backpropagation *** study’s objective is to determine the best activation functions for the approximation of each fractal *** results have been attained using Matlab and Visual Basic programs,which indi-cate that the bounded function is more helpful than other *** non-lin-earity of the activation function is important when using neural networks for coding fractal images because the coefﬁcients of the Iterated Function System are different according to the different types of *** most commonly cho-sen activation function is the sigmoidal function,which produces a positive *** functions,such as tansh or arctan,whose values can be positive or negative depending on the network input,tend to train neural networks *** coding speed of the fractal image is different depending on the appropriate activation function chosen for each fractal *** this paper,we have provided the appro-priate activation functions for each type of system of iterated functions that help the network to identify the transactions of the system.

关键词： Activation function fractal coding iterated function system

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Fractal Image Compression Using Self-Organizing Mapping

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Applied Mathematics 2014年第12期5卷 1810-1819页

作者： rashad A. al-jawfi Baligh M. al-Helali Adil M. Ahmed Department of Mathematics and Computer Science Faculty of Science Ibb University Ibb Yemen Department of Mathematics Faculty of Sciences and Arts Nauran University KSA Department of Mathematics Faculty of Ibn Alhaitham for Education Baghdad University Baghdad Iraq

One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathematical idea of fractal image compression. Then this idea is applied by iterated function system, iterative system functions and grayscale iterated function system down to general transformation. Mathematical formulation form is also provided on the digital image space, which deals with the computer. Next, this process has been revised to reduce the time required for image compression by excluding some parts of the image that have a specific milestone. The neural network algorithms have been applied on the process of compression (encryption). The experimental results are presented and the performance of the proposed algorithm is discussed. Finally, the comparison between filtered ranges method and self-organizing method is introduced.

关键词： Fractal Image Compression Organizing Mapping

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