CONSTRUCTION OF A NEUROET NETWORK EXPERT SYSTEM FOR PROCESSING NAVIGATION DATA IN CONDITIONS RIVER е-NAVIGATION
Науковий журнал «Радіоелектроніка, інформатика, управління»
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CONSTRUCTION OF A NEUROET NETWORK EXPERT SYSTEM FOR PROCESSING NAVIGATION DATA IN CONDITIONS RIVER е-NAVIGATION |
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Panin, V. V.; State University of Infrastructure and Technologies, Kyiv, Ukraine Doronin, V. V.; State University of Infrastructure and Technologies, Kyiv, Ukraine Spiian, O. M.; State University of Infrastructure and Technologies, Kyiv, Ukraine |
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sample; expert system; e-navigation; fuzzy-neural model; use case; Inland ECDIS |
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<p>Context. The diagnostics automatisation problem of network anomalies during navigation data processing from gauging stations to electronic chart system under river e-navigation was studied. The object of the study is a process of diagnostic in the dynamic expert system. The purpose of the research is creating of automated troubleshooting system with a help of neural system.<br />Method. The diagnostic automatisation method of network anomalies with context-based intelligent navigation data processing usage was suggested. The main idea consists in modern data processing methods usage in the neural system. These methods are based on the fuzzy logic algorithm. Neural networks can be in operation during parameter fluctuations, that come from gauging stations. The cluster-rules set is displayed in the fuzzy neural system structure. There is no need to download all sample specifications in the electronic mapping system or to re-check the sample, it helps to speed up the process of network synthesis. In<br />the navigation data processing system were used different expert systems and neural networks. Data processing system should find network anomalies and propose the ways of their decision size, the method allows to get different levels of sample specification. The method allows to minimize network error in the synthesized model. In addition to fractal method, also had been proposed a method for unknown regularities between the input and output data coming from the gauging stations. That is, the neural network can determine which signals are non-informative. With a help of input data classification from the gauging stations based on Kokhonen`s system, the space of the data stream splits into clusters of the same size and shape. By changing the cluster size, the method allows to<br />get different levels of sample specification. The procedure of input signals classification helps to predict the increasing or decreasing of differential corrections towards depth, and recognize information from gauging stations. The method determines the encoding and decoding of navigation parameters by specifying the parametric function of the triangular shape. The rules formed by an expert<br />knowledge were established. In order to keep intellectual system under current conditions should be used neural dynamic expert system model including use case. The mechanism of automated solution based on the search use case algorithm is defined. If there are not any use cases, the task solves with a help of neural network rules. The network nodes are neurons – particular facts that are<br />consequences of use case. Links between nodes of the network implement the rules. That is, a multilayer neural network of facts and rules is built up.<br />Results. The experimental indexes of network anomalies during data processing from the gauging stations were followed up.<br />Conclusions. The automatisation problem of network diagnostic anomalies with a help of flexible fuzzy neural network was solved. The conducted experiments confirmed the efficiency of the proposed methods. Further research may consist in the creation of an instrumental navigation method (river e-navigation).</p> |
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Zaporizhzhya National Technical University 2019-04-16 11:21:23 |
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application/pdf http://ric.zntu.edu.ua/article/view/164062 |
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Radio Electronics, Computer Science, Control; No 1 (2019): Radio Electronics, Computer Science, Control |
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uk |
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Copyright (c) 2019 V. V. Panin, V. V. Doronin, O. M. Spiian |
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