By Topic

An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Guang-Bin Huang ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; P. Saratchandran ; N. Sundararajan

This work presents a simple sequential growing and pruning algorithm for radial basis function (RBF) networks. The algorithm referred to as growing and pruning (GAP)-RBF uses the concept of "Significance" of a neuron and links it to the learning accuracy. "Significance" of a neuron is defined as its contribution to the network output averaged over all the input data received so far. Using a piecewise-linear approximation for the Gaussian function, a simple and efficient way of computing this significance has been derived for uniformly distributed input data. In the GAP-RBF algorithm, the growing and pruning are based on the significance of the "nearest" neuron. In this paper, the performance of the GAP-RBF learning algorithm is compared with other well-known sequential learning algorithms like RAN, RANEKF, and MRAN on an artificial problem with uniform input distribution and three real-world nonuniform, higher dimensional benchmark problems. The results indicate that the GAP-RBF algorithm can provide comparable generalization performance with a considerably reduced network size and training time.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:34 ,  Issue: 6 )