Parallel guessing: a strategy for high-speed computation
Pattern Recognition
Neurocomputing: foundations of research
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A new EM-based training algorithm for RBF networks
Neural Networks
On the Kernel Widths in Radial-Basis Function Networks
Neural Processing Letters
Fast learning in networks of locally-tuned processing units
Neural Computation
Expert Systems with Applications: An International Journal
Divided two-part adaptive intrusion detection system
Wireless Networks
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This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function (RBF) neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types, processing their rotor sounds. The first method uses an optimum learning rate in each iteration of training process. This method increases the speed of learning process and also achieves an absolute stability in network response. Another modification is applied to quick propagation (QP) method as a generalization that attains more learning speed. Finally, we introduced the general optimum steepest descent (GOSD) method, which contains both improvements in learning RBF networks. All modified methods are employed in training a system that recognizes helicopters' rotor sounds exploiting a RBF neural network. Comparing results of these learning methods with the previous ones yields interesting outcomes.