Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures

László Kovács ()
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László Kovács: Institute of Informatics, University of Miskolc, H-3515 Miskolc, Hungary

Mathematics, 2025, vol. 13, issue 9, 1-25

Abstract: The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures.

Keywords: redial basis function; neural networks; parameter initialization; density-based entropy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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