Performance Evaluation of GAP-RBF Network in Channel Equalization
Neural Processing Letters
No-reference JPEG-image quality assessment using GAP-RBF
Signal Processing
Improved GAP-RBF network for classification problems
Neurocomputing
Pruning RBF networks with QLP decomposition
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Expert Systems with Applications: An International Journal
Small Number of Hidden Units for ELM with Two-Stage Linear Model
IEICE - Transactions on Information and Systems
A Hybrid MPSO-BP-RBFN Model for Reservoir Lateral Prediction
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
An Online Self-constructing Fuzzy Neural Network with Restrictive Growth
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
Evolving logic networks with real-valued inputs for fast incremental learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A growing and pruning method for radial basis function networks
IEEE Transactions on Neural Networks
A constructive enhancement for online sequential extreme learning machine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Online adaptive radial basis function networks for robust object tracking
Computer Vision and Image Understanding
Deformable radial basis functions
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A fast multi-output RBF neural network construction method
Neurocomputing
Online training for single hidden-layer Online training for single hidden-layer
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A new online learning algorithm for structure-adjustable extreme learning machine
Computers & Mathematics with Applications
Error tolerance based support vector machine for regression
Neurocomputing
Approximation of Gaussian basis functions in the problem of adaptive control of nonlinear objects
Cybernetics and Systems Analysis
Online learning neural tracker
Neurocomputing
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network
Neural Processing Letters
Vision-Based Fingertip-Writing Character Recognition
Journal of Signal Processing Systems
Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks
Pattern Recognition Letters
GAP-RBF based NR image quality measurement for JPEG coded images
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Intelligent control of a constant turning force system with fixed metal removal rate
Applied Soft Computing
Automatica (Journal of IFAC)
A novel self-constructing Radial Basis Function Neural-Fuzzy System
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
Hi-index | 0.01 |
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.