NDLI: A Novel Structure for Radial Basis Function Networks

Content Provider	Springer Nature Link
Author	Khosravi, Hossein
Copyright Year	2011
Abstract	A novel structure for radial basis function networks is proposed. In this structure, unlike traditional RBF, we set some weights between input and hidden layer. These weights, which take values around unity, are multiplication factors for input vector and perform a linear mapping. Doing this, we increase free parameters of the network, but since these weights are trainable, the overall performance of the network is improved significantly. According to the new weight vector, we called this structure Weighted RBF or WRBF. Weight adjustment formula is provided by applying the gradient descent algorithm. Two classification problems used to evaluate performance of the new RBF network: letter classification using UCI dataset with 16 features, a difficult problem, and digit recognition using HODA dataset with 64 features, an easy problem. WRBF is compared with classic RBF and MLP network, and our experiments show that WRBF outperforms both significantly. For example, in the case of 200 hidden neurons, WRBF achieved recognition rate of 92.78% on UCI dataset while RBF and MLP achieved 83.13 and 89.25% respectively. On HODA dataset, WRBF reached 97.94% recognition rate whereas RBF achieved 97.14%, and MLP accomplished 97.63%.
Starting Page	177
Ending Page	186
Page Count	10
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	35
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2011-12-29
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Radial basis Neural network RBF WRBF Classification Gradient descent Statistical Physics, Dynamical Systems and Complexity Computational Intelligence Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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