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Second international workshop on Intelligent systems design and application
A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
New fuzzy wavelet neural networks for system identification and control
Applied Soft Computing
Letters: Training RBF network to tolerate single node fault
Neurocomputing
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Mathematics and Computers in Simulation
Choquet fuzzy integral based verification of handwritten signatures
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The Widrow-Hoff delta rule is one of the most popular rules used in training neural networks. It was originally proposed for the ADALINE, but has been successfully applied to a few nonlinear neural networks as well. Despite its popularity, there exist a few misconceptions on its convergence properties. We consider repetitive learning (i.e., a fixed set of samples are used for training) and provide an in-depth analysis in the least mean square (LMS) framework. Our main result is that contrary to common belief, the nonbatch Widrow-Hoff rule does not converge in general. It converges only to a limit cycle