Performance analysis of the activation neuron function in the Flexible Neural Tree model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86096576" target="_blank" >RIV/61989100:27240/14:86096576 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/14:86096576
Result on the web
<a href="http://ceur-ws.org/Vol-1139/paper4.pdf" target="_blank" >http://ceur-ws.org/Vol-1139/paper4.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Performance analysis of the activation neuron function in the Flexible Neural Tree model
Original language description
The time series prediction and forecasting is an important area in the field of Machine Learning. Around ten years ago, a kind of Multilayer Neural Network was introduced under the name of Flexible Neural Tree (FNT). This model uses meta-heuristic techniques to determinate its topology and its embedded parameters. The FNT model has been successfully employed on time-series modeling and temporal learning tasks. The activation function used in the FNT belongs to the family of radial basis functions. It isa parametric function and the parameters are set employing an heuristic procedure. In this article, we analyze the impact on the performance of the FNT model when it used other family of neuron activation functions. For that, we use the hyperbolic tangent and Fermi activation functions on the tree nodes. Both functions have been extensively used in the field of Neural Networks. Moreover, we study the FNT technique with a linear variant of the Fermi function. We present an experimental c
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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