Improving the flexible neural tree model with swarm intelligence
by Tomáš Buriánek; Sebastián Basterrech
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 25, No. 3/4, 2023

Abstract: A type of feedforward neural network with a specific architecture was developed around ten years ago under the name of flexible neural tree (FNT). The model has two families of adjustable parameters: the parameters presented in the activation function of the neurons, and the topology of the tree. The method uses meta-heuristic algorithms for finding a good tree topology and the set of embedded parameters. The technique has been successfully applied for solving machine learning problems with time-series and sequential data. The canonical FNT was introduced with the radial basis function as activation function of the neurons. In this article, we analyse the performance of the FNT when different type of activation functions is presented in the tree. We present a comparative analysis among different type of neurons. We study the performance of the model when the following four types of neurons are used: Gaussian, hyperbolic tangent, Fermi function and a linear variation of Fermi function. The empirical analysis was made over a well-known simulated time-series benchmark and a real-world networking problem.

Online publication date: Wed, 19-Jul-2023

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