Abstract
Radial Basis Function Neural Networks (RBFNs) are quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. The performance of the RBF network depends on numerous factors. One of them is a value of the RBF shape parameter. This parameter has a direct impact on performance of the transfer function of each hidden unit. Values of the transfer function parameters, including the value of its shape, are set during the RBFN tuning phase. Setting values of the transfer function parameters, including its shape can be viewed as the optimization problem in which the performance of the considered RBFN is maximized. In the paper the agent-based population learning algorithm finding the optimal or near optimal value of the RBF shape parameter is proposed and evaluated.
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Keywords
- Transfer Function
- Radial Basis Function
- Radial Basis Function Neural Network
- Radial Basis Function Network
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Czarnowski, I., Jędrzejowicz, P. (2013). Agent-Based Population Learning Algorithm for RBF Network Tuning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_4
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DOI: https://doi.org/10.1007/978-3-642-38658-9_4
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