Abstract
This paper proposes an approach for the multivariate modelling of time series with neuro-fuzzy systems. The fuzzy rule model is based on adaptive B-splines which can approximate any given input-output data series of low dimension. To efficiently describe high-dimensional input data, statistical indices are extracted to feed the fuzzy controller. The original input space is transformed into an eigenspace. If a sequence of training data are sampled in a local context, a small number of eigenvectors which possess larger eigenvalues provide a good summary of all the original variables. Fuzzy controllers can be trained for mapping the input projection in the eigenspace to the outputs. Experiments of time series prediction validate the concept.
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© 2001 Physica-Verlag Heidelberg
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Zhang, J., Knoll, A. (2001). Neuro-Fuzzy Modelling of Time Series. In: Ruan, D., Kacprzyk, J., Fedrizzi, M. (eds) Soft Computing for Risk Evaluation and Management. Studies in Fuzziness and Soft Computing, vol 76. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1814-7_9
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DOI: https://doi.org/10.1007/978-3-7908-1814-7_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00348-0
Online ISBN: 978-3-7908-1814-7
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