Parallel guessing: a strategy for high-speed computation
Pattern Recognition
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural networks for pattern recognition
Neural networks for pattern recognition
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Three learning phases for radial-basis-function networks
Neural Networks
A comparative analysis of neural network performances in astronomical imaging
Applied Numerical Mathematics
Fast learning in networks of locally-tuned processing units
Neural Computation
Deformation Measurement of the Large Flexible Surface by Improved RBFNN Algorithm and BPNN Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Learning methods for radial basis function networks
Future Generation Computer Systems
Least-mean-square training of cluster-weighted modeling
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A real-time automated system for the recognition of human facial expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In this paper a new methodology for training radial basis function (RBF) neural networks is introduced and examined. This novel approach, called Fuzzy-OSD, could be used in applications, which need real-time capabilities for retraining neural networks. The proposed method uses fuzzy clustering in order to improve the functionality of the Optimum Steepest Descent (OSD) learning algorithm. This improvement is due to initialization of RBF units more precisely using fuzzy C-Means clustering algorithm that results in producing better and the same network response in different retraining attempts. In addition, adjusting RBF units in the network with great accuracy will result in better performance in fewer train iterations, which is essential when fast retraining of the network is needed, especially in the real-time systems. We employed this new method in an online radar pulse classification system, which needs quick retraining of the network once new unseen emitters detected. Having compared result of applying the new algorithm and Three-Phase OSD method to benchmark problems from Proben1 database and also using them in our system, we achieved improvement in the results as presented in this paper.