Abstract
Piecewise linear models are attractive when modeling a wide range of nonlinear phenomena but determining simultaneously the domain decomposition and the corresponding parameter values is a challenging problem. We show that this problem can be formulated as that of partitioning an inconsistent linear system into a minimum number of consistent subsystems and we describe a greedy algorithm, based on a simple variant of the perceptron procedure, which provides good approximate solutions in a short amount of time. The general approach is applied to piecewise linear modeling of time series.
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© 1997 Springer-Verlag Berlin Heidelberg
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Mattavelli, M., Amaldi, E., Vesin, JM. (1997). A perceptron-based approach to piecewise linear modeling with an application to time series. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020211
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DOI: https://doi.org/10.1007/BFb0020211
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