jnaiamcont.org  Issue 12
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PRINCIPAL DIRECTIONS OF DEVELOPING THE DESIGN METHODS FOR INTELLIGENT SYSTEMS TO CONTROL ROBOTS
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V.V. Kozhevnikov<sup>1</sup>, V.V. Prikhodko<sup>1</sup>, V.V. Svetukhin<sup>1</sup>, A.V. Zhukov<sup>1</sup>, A.N. Fomin<sup>1</sup>, M.Yu. Leontyev<sup>1</sup>, D.Ya. Vostretsov<sup>1</sup>, A.A. Sobolev<sup>1</sup>, V.I. Skrebtsov<sup>1</sup>, V.E. Kiryukhin<sup>1</sup>, V.V. Levschanov<sup>1</sup>, D.S. Lavygin<sup>1</sup>, E.M. Chavkin<sup>1</sup>, E.R. Mingachev<sup>1</sup>, R.G. Bildanov<sup>1</sup>, S.V. Pavlov<sup>2</sup>, V.N. Kovalnogov<sup>1</sup></p>
<p align="center"> <sup>1</sup> S.P. Kapitsa Research Institute of Technology (Technological Research Institute) of Ulyanovsk State University, Ulyanovsk, Russia<br /><sup>2</sup> Sosny Research and Development Company, Dimitrovgrad, Ulyanovsk region, Russia<br /></p>
<p align="center">Received 29 June, 2018; accepted in revised form 17 July, 2018</p>
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<p> <strong>Abstract</strong><br />Principal directions of developing the methods for designing intelligent systems of robot control assume technologies based on the use of artificial neural networks. The neural networks, where the model of a neuron was developed as the simplest processor element, performing the computation of the transfer function of a scalar product of an input data vector and a weight vector, can give interesting results regarding generation of dependencies and forecasting. However, their obvious drawback is the lack of an explicit algorithm of action. Memorization of information in the learning process occurs implicitly as a result of selection of the weight coefficients of the neural network, therefore the problem of cognition (the formation of new knowledge) on the basis of those obtained earlier in the learning process seems difficult to resolve. A positive solution to this problem will open the way to the creation of the fullfledged artificial mind. From this point of view the promising area is where the mathematical model of the neural networks is built on the basis of mathematical logic. The intelligent control system in this case is a software and hardware complex, where the mathematical model of the neural network identifies the control system as an intellectual one.</p>
<p align="center"> c 2018 European Society of Computational Methods in Sciences and Engineering </p>
<p align="center"> <strong><a href="http://jnaiam.org/exit.php?url_id=214&entry_id=129" title="https://drive.google.com/open?id=1dMnqUjJ_MaDffmPCJwd5k54c9g2y31x" onmouseover="window.status='https://drive.google.com/open?id=1dMnqUjJ_MaDffmPCJwd5k54c9g2y31x';return true;" onmouseout="window.status='';return true;">Download Full PDF</a></strong></p>
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Mon, 16 Jul 2018 05:48:15 +0000
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Toward a preconditioned scalable 3DVAR for assimilating Sea Surface Temperature collected into the Caspian Sea
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<p>R. Arcucci<sup>ab1</sup>, L.Carracciuolo<sup>c</sup> and R.Toumi<sup>d</sup><br />a: University of Naples Federico II, Naples, Italy<br />b: Euro Mediterranean Center on Climate Change, Italy<br />c: National Research Council, Naples, Italy<br />d: Imperial College London, London, United Kingdom</p>
<p><br />Received 1 February, 2017; accepted in revised form 03 April, 2018
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<p>Abstract: Data Assimilation (DA) is an uncertainty quantication technique used to<br />incorporate observed data into a prediction model in order to improve numerical forecasted<br />results. As a crucial point into DA models is the ill conditioning of the covariance matrices<br />involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here<br />we present rst results obtained introducing two dierent preconditioning methods in<br />a DA software we are developing (we named S3DVAR) which implements a Scalable<br />Three Dimensional Variational Data Assimilation model for assimilating sea surface<br />temperature (SST) values collected into the Caspian Sea by using the Regional Ocean<br />Modeling System (ROMS) with observations provided by the Group of High resolution<br />sea surface temperature (GHRSST). We present the algorithmic strategies we employ<br />and the numerical issues on data collected in two of the months which present the most<br />signicant variability in water temperature: August and March. </p>
<p><br />c 2018 European Society of Computational Methods in Sciences and Engineering </p>
<p>Keywords: Data Assimilation, oceanographic data, Sea Surface Temperature, Caspian sea,<br />ROMS<br />Mathematics Subject Classication: 65Y05, 65J22, 68W10, 68U20<br />PACS: 02.70.c</p>
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<p><strong><a href="http://jnaiam.org/exit.php?url_id=212&entry_id=128" title="http://www.jnaiam.org/uploads/Volume_12_Issue_12_pp_928.pdf" onmouseover="window.status='http://www.jnaiam.org/uploads/Volume_12_Issue_12_pp_928.pdf';return true;" onmouseout="window.status='';return true;">Download Full PDF</a></strong></p>
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Fri, 06 Apr 2018 14:21:58 +0000
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Application of the Fractional Calculus to the Radial Schrödinger Equation given by the Makarov Potential
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<p><font size="4"><strong>O. Ozturk</strong></font><br /><em>Department of Mathematics,<br />Faculty of Arts and Sciences,<br />Bitlis Eren University,<br />13000 Bitlis, Turkey</em></p>
<p><em></em><br />Received 10 October, 2016; accepted in revised form 22 March, 2018</p>
<p><br />Abstract: Fractional calculus and its generalizations are used for the solutions of some classes of differential<br />equations and fractional differential equations. In this paper, our aim is to solve the radial Schrödinger<br />equation given by the Makarov potential by the help of fractional calculus theorems. The related equation<br />was solved by applying a fractional calculus theorem that gives fractional solutions of the second order<br />differential equations with singular points. In the last section, we also introduced hypergeometric form of<br />this solution.</p>
<p><br />© European Society of Computational Methods in Sciences and Engineering<br />Keywords: Fractional calculus, Generalized Leibniz rule, Radial Schrödinger equation, Makarov potential<br />Mathematics Subject Classification: 26A33, 34A08</p>
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<p><strong><a href="http://jnaiam.org/exit.php?url_id=210&entry_id=127" onmouseover="window.status='http://www.jnaiam.org/uploads/Volume_12_Issue_12_pp_18.pdf';return true;" onmouseout="window.status='';return true;" title="f406b9b2dc77ddf92b617574e24caf93.pdf" target="_blank">Download Full PDF</a></strong></p>
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Mon, 26 Mar 2018 12:20:31 +0000
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