Kernel Width Optimization for Faulty RBF Neural Networks with Multi-node Open Fault

Abstract

Many researches have been devoted to select the kernel parameters, including the centers, kernel width and weights, for fault-free radial basis function (RBF) neural networks. However, most are concerned with the centers and weights identification, and fewer focus on the kernel width selection. Moreover, to our knowledge, almost no literature has proposed… (More)
DOI: 10.1007/s11063-010-9145-x

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