Abstract
In this paper an extension of the established training algorithm for nonlinear system identification called Lolimot is presented [9]. It is a heuristic tree-construction method that trains a local linear neuro-fuzzy network. Due to its very simple partitioning strategy, Lolimot is a fast and robust modeling approach, but has a limited flexibility. Therefore a new merging approach for regression tasks is presented, that can rearrange the local model structure in the input space, without harming the global model complexity.
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Fischer, T., Nelles, O. (2014). Merging Strategy for Local Model Networks Based on the Lolimot Algorithm. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_20
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DOI: https://doi.org/10.1007/978-3-319-11179-7_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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