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    Treatment of multi-dimensional data to enhance neural network estimators in regression problems
    (PERGAMON-ELSEVIER SCIENCE LTD, 2007) Altun, H.; Bilgil, A.; Fidan, B. C.
    This paper proposes and explains a data treatment technique to improve the accuracy of a neural network estimator in regression problems, where multi-dimensional input data set is highly skewed and non-normally distributed. The proposed treatment modifies the distribution characteristics of the data set. The prediction of the suspended sediment, which is an important problem in river engineering applications, will be considered as a case study. Conventional approaches lack in providing high accuracy due to the inherently employed simplicity in order to obtain empirical formulae. On the other hand, artificial neural networks are able to model the non-linear characteristics of the mechanism of the sediment transport and have a growing body of applications in diverse applications in civil engineering. It will be shown that a significant enhancement and superior score in accuracy, compared with the classical approaches, are obtainable when the proposed treatment is employed. The proposed technique is an extension to the understanding of the practical aspects of neural computing applications. Therefore the outcome of the present study is important as it is applicable to any scenario where neural network approaches are involved. (C) 2006 Elsevier Ltd. All rights reserved.
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    Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models
    (PERGAMON-ELSEVIER SCIENCE LTD, 2007) Altun, H.; Bilgil, A.; Fidan, B. C.
    Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values. (c) 2006 Elsevier Ltd. All rights reserved.