Please use this identifier to cite or link to this item: http://repository.aaup.edu/jspui/handle/123456789/291
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dc.contributor.authormohammed awad
dc.date.accessioned2020-02-02T13:05:19Z-
dc.date.available2020-02-02T13:05:19Z-
dc.date.issued2015
dc.identifier.urihttp://repository.aaup.edu/jspui/handle/123456789/291-
dc.description.abstractTime series forecasting is an important tool, which is used to support the areas of planning for both individual and organizational decisions. This problem consists of forecasting future data based on past and/or present data. This paper deals with the problem of time series forecasting from a given set of input/output data. We present a hybrid approach for time series forecasting using Radial Basis Functions Neural Network (RBFNs) and Genetic Algorithms (GAs). GAs technique proposed to optimize centers c and width r of RBFN, the weights w of RBFNs optimized used traditional algorithm. This method uses an adaptive process of optimizing the RBFN parameters depending on GAs, which improve the homogenize during the process. This proposed hybrid approach improves the forecasting performance of the time series. The performance of the proposed method evaluated on examples of short-term mackey-glass time series. The results show that forecasting by RBFNs parameters is optimized using GAs to achieve better root mean square error than algorithms that optimize RBFNs parameters found by traditional algorithms
dc.publisherThe International Arab Journal of Information Technology.
dc.subjectTime series forecasting
dc.subjectRBF neural networks
dc.subjectGenetic Algorithms
dc.subjectHybrid Approach
dc.titleForecasting of Chaotic Time Series Using RBF Neural Networks Optimized By Genetic Algorithms
dc.typeArticle
Appears in Collections:Faculty & Staff Scientific Research publications

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