Abstract
Like multi-layer perceptrons, radial basis function networks are feed-forward neural networks with a strictly layered structure. However, the number of layers is always three, that is, there is exactly one hidden layer. In addition, radial basis function networks differ from multi-layer perceptrons in the network input and activation functions, especially in the hidden layer. In this hidden layer radial basis functions are employed, which are responsible for the name of this type of neural network. With these functions a kind of “catchment region” is assigned to each neuron, in which it mainly influences the output of the neural network.
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© 2013 Springer-Verlag London
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Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P. (2013). Radial Basis Function Networks. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5013-8_6
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DOI: https://doi.org/10.1007/978-1-4471-5013-8_6
Publisher Name: Springer, London
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