A Self-adaptive Growing Method for Training Compact RBF Networks

Radial Basis Function (RBF) network is a neural network model widely used for supervised learning tasks. The prediction time of a RBF network is proportional to the number of nodes in its hidden layer, while there is also a positive correlation between the number of nodes and the predication accuracy. In this paper, we propose a new training algorithm for RBF networks in order to construct high accuracy networks with as few nodes as possible. The proposed method starts with an empty network, selecting a best node from candidates iteratively until the training error reduces to a threshold or the number of nodes reaches a limit. Then the network is further optimized with a supervised fine-tuning method. Experimental results indicate that the proposed method could achieve better performances than traditional algorithms when training same sized RBF networks.

[1]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[2]  Maria Bortman,et al.  A Growing and Pruning Method for Radial Basis Function Networks , 2009, IEEE Transactions on Neural Networks.

[3]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[4]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.

[5]  Hao Yu,et al.  An Incremental Design of Radial Basis Function Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[6]  P. J. Werbos,et al.  Backpropagation: past and future , 1988, IEEE 1988 International Conference on Neural Networks.

[7]  Moncef Gabbouj,et al.  Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering , 2016, IEEE Transactions on Neural Networks and Learning Systems.