Fuzzy Backpropagation Neural Networks for Nonstationary Data Prediction

Authors:
Ramon Soto C.
Affiliations:
Centro de Investigación y Desarrollo de Ingeniería Avanzada, Real #30 Col. Villa Satélite, Hermosillo, Sonora, Mexico
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

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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.