Abstract
A new learning algorithm for solving piecewise linear regression problems is proposed. It is able to train a proper multilayer feedforward neural network so as to reconstruct a target function assuming a different linear behavior on each set of a polyhedral partition of the input domain.
The proposed method combine local estimation, clustering in weight space, classification and regression in order to achieve the desired result. A simulation on a benchmark problem shows the good properties of this new learning algorithm.
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Ferrari-Trecate, G., Muselli, M., Liberati, D., Morari, M. (2002). A Learning Algorithm for Piecewise Linear Regression. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_9
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DOI: https://doi.org/10.1007/978-1-4471-0219-9_9
Publisher Name: Springer, London
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