HYBRID NEURON NETWORKS BASED ON RADIAL BASIS NETWORK WITH DIFFERENT RADIAL BASIS FUNCTION
DOI:
https://doi.org/10.18372/1990-5548.63.14516Keywords:
Hybrid neural network, structural-parametric synthesis, optimization problemAbstract
It is considered the problem of structural-parametric synthesis of a hybrid neural networks based on the use of radial basis network. Hybridization is achieved through the use of various radial basis functions: Gaussian, multivariate, inverse quadratic, inverse multivariate, thin plate spline, linear, cubic, wavelet functions. The problem of structural-parametric synthesis of hybrid neural network consists in the optimal choice of the number of layers, the number of neurons in the layers, the order of alternation of layers with different neurons. The problem of optimal choice of the number of network cascades and the type of radial basis function in each cascade of the number of layers is solved. Examples of a hybrid neural network synthesis using this methodology for classification and prediction tasks solution are given.References
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