Abstract
In this paper, a new time-series forecasting model based on the Flexible Beta Operator Neural Tree (FBONT) is introduced. The FBONT model which has a tree-structural representation is considered as a special Beta basis function multi-layer neural network. Based on the pre-defined Beta operator sets, the FBONT can be formed and optimized. The FBONT structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Particle Swarm Optimization algorithm (PSO). The performance of the proposed method is evaluated using time series forecasting problems and compared with those of related methods.
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Bouaziz, S., Dhahri, H., Alimi, A.M. (2012). Evolving Flexible Beta Operator Neural Trees (FBONT) for Time Series Forecasting. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_3
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DOI: https://doi.org/10.1007/978-3-642-34487-9_3
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