Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm
- 1 March 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 9 (2) , 308-318
- https://doi.org/10.1109/72.661125
Abstract
This paper presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data.Keywords
This publication has 16 references indexed in Scilit:
- Identification of time-varying nonlinear systems using minimal radial basis function neural networksIEE Proceedings - Control Theory and Applications, 1997
- A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural NetworksNeural Computation, 1997
- The dependence identification neural network construction algorithmIEEE Transactions on Neural Networks, 1996
- Minimal Topology for a Radial Basis Functions Neural Network for Pattern ClassificationDigital Signal Processing, 1994
- A Function Estimation Approach to Sequential Learning with Neural NetworksNeural Computation, 1993
- On the training of radial basis function classifiersNeural Networks, 1992
- Recursive hybrid algorithm for non-linear system identification using radial basis function networksInternational Journal of Control, 1992
- A Resource-Allocating Network for Function InterpolationNeural Computation, 1991
- An introduction to computing with neural netsIEEE ASSP Magazine, 1987
- Learning representations by back-propagating errorsNature, 1986