Efficient design of multicolumn RBF networks

To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Neurocomputing (Amsterdam) Ročník 450; s. 253 - 263
Hlavní autori: Han, Ziyang, Qian, Xusheng, Huang, He, Huang, Tingwen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 25.08.2021
Predmet:
Efficient learning
Generalized hybrid constructive algorithm
Minimum difference scheme
Multicolumn RBF networks
Parallel structure
Potential centers
Generalized hybrid constructive algorithm
Efficient learning
Multicolumn RBF networks
Parallel structure
Potential centers
Minimum difference scheme
ISSN:0925-2312, 1872-8286
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks are integrated as a parallel structure to obtain the final model. This paper focuses on presenting an efficient design method for MCRN based on improved generalized hybrid constructive (GHC) learning algorithm such that desired performance and high efficiency are guaranteed. Specifically, a minimum difference scheme is firstly introduced to divide a large-scale dataset into several parts with similar distribution. For each subtask, a compact RBF network is designed by improved GHC learning algorithm. By fully considering the inherent properties of RBF networks, a K-nearest potential centers based criterion is established to calculate the output of testing sample. Experiments on some benchmark classification problems are conducted to verify the advantages of the proposed method. In terms of training and testing efficiencies and classification accuracy, it is shown that our model is superior over some existing ones.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2021.04.040