Abstract
The backpropagation neural network is one of the most widely used connectionist model, especially in the solution of real life problems. The main reasons for the popularity of this model are its conceptual simplicity and its ability to tackle a broad range of problems. But, on the other hand, this architecture shows a well known problem for dealing with nonstationary data. In this paper, a variation of feedforward neural model which uses qualitative data both for feeding the network and for back propagating the error correction is presented. The data are coded by means of a fuzzy concept of local stability.
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Soto C., R. (2007). Fuzzy Backpropagation Neural Networks for Nonstationary Data Prediction. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_32
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DOI: https://doi.org/10.1007/978-3-540-72950-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72917-4
Online ISBN: 978-3-540-72950-1
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