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A parallel Numerical Algorithm For Solving Some Fractional Integral Equations
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In this study, He's parallel numerical algorithm by neural network is applied to type of integration of fractional equations is Abel’s integral equations of the 1st and 2nd kinds. Using a Levenberge – Marquaradt training algorithm as a tool to train the network. To show the efficiency of the method, some type of Abel’s integral equations is solved as numerical examples. Numerical results show that the new method is very efficient problems with high accuracy.

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Publication Date
Mon Mar 01 2021
Journal Name
Journal Of Physics: Conference Series
Some Results in Grűnwald-Letnikov Fractional Derivative and its Best Approximation
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Abstract<p>In This paper, we have been approximated Grűnwald-Letnikov Derivative of a function having m continuous derivatives by Bernstein Chlodowsky polynomials with proving its best approximation. As well as we have been solved Bagley-Torvik equation and Fokker–Planck equation where the derivative is in Grűnwald-Letnikov sense.</p>
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Publication Date
Thu Jun 01 2017
Journal Name
Chaos, Solitons &amp; Fractals
A semi-analytical iterative method for solving nonlinear thin film flow problems
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Publication Date
Wed Jan 01 2014
Journal Name
Siam Journal On Control And Optimization
A Duality Approach for Solving Control-Constrained Linear-Quadratic Optimal Control Problems
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Publication Date
Tue May 16 2023
Journal Name
Journal Of Engineering
Parallel Routing in Wireless Sensor Network
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The limitations of wireless sensor nodes are power, computational capabilities, and memory. This paper suggests a method to reduce the power consumption by a sensor node. This work is based on the analogy of the routing problem to distribute an electrical field in a physical media with a given density of charges. From this analogy a set of partial differential equations (Poisson's equation) is obtained. A finite difference method is utilized to solve this set numerically. Then a parallel implementation is presented. The parallel implementation is based on domain decomposition, where the original calculation domain is decomposed into several blocks, each of which given to a processing element. All nodes then execute computations in parall

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Publication Date
Fri Dec 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN
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Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

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Publication Date
Mon Jul 01 2019
Journal Name
2019 International Joint Conference On Neural Networks (ijcnn)
A Fast Feature Extraction Algorithm for Image and Video Processing
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Publication Date
Fri Apr 01 2011
Journal Name
Al-mustansiriyah Journal Of Science
A Genetic Algorithm Based Approach For Generating Unit Maintenance Scheduling
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Publication Date
Thu May 01 2008
Journal Name
2008 International Conference On Computer And Communication Engineering
A binary Particle Swarm Optimization for attacking knapsacks Cipher Algorithm
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Publication Date
Fri Mar 01 2019
Journal Name
Far East Journal Of Mathematical Sciences (fjms)
SOME TYPES OF DELAY DIFFERENTIAL EQUATIONS SOLVED BY SUMUDU TRANSFORM METHOD
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Publication Date
Tue Jun 18 2024
Journal Name
2024 Ieee 33rd International Symposium On Industrial Electronics (isie)
An Adaptive Integral Sliding Mode Control for Disturbed Servo Motor Systems
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Abstract-Servo motors are important parts of industry automation due to their several advantages such as cost and energy efficiency, simple design, and flexibility. However, the position control of the servo motor is a difficult task because of different factors of external disturbances, nonlinearities, and uncertainties. To tackle these challenges, an adaptive integral sliding mode control (AISMC) is proposed, in which a novel bidirectional adaptive law is constructed to reduce the control chattering. The proposed control has three steps to be designed. Firstly, a full-order integral sliding manifold is designed to improve the servo motor position tracking performance, in which the reaching phase is eliminated to achieve the invariance of

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