Robust regression and outlier detection
Robust regression and outlier detection
Topology representing networks
Neural Networks
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
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Most supervised neural networks are trained by minimizing the mean square error (MSE) of the training set. In the presence of outliers, the resulting neural network model can differ significantly from the underlying model that generates the data. This paper outlines two robust learning methods for a dynamic structure neural network called incremental growing multi-experts network (IGMN). It is convincingly shown by simulation that by using a scaled robust objective function instead of the least squares function, the influence of the outliers in the training data can be completely eliminated. The network generates a much better approximation in the neighborhood of outliers. Thus, the two proposed robust learning methods namely robust least mean squares (RLMSs) and least mean log squares (LMLSs) are insensitive to the presence of outliers unlike the least mean squares (LMSs) cost function. Moreover, various types of supervised learning algorithms can easily adopt LMLS, which is a parameter-free method.