TAO-robust backpropagation learning algorithm
Neural Networks
International Journal of Approximate Reasoning
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
Robust incremental growing multi-experts network
Applied Soft Computing
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
Expert Systems with Applications: An International Journal
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
Outliers detection in environmental monitoring databases
Engineering Applications of Artificial Intelligence
An energy-gain bounding approach to robust fuzzy identification
Automatica (Journal of IFAC)
Adaptive ore grade estimation method for the mineral deposit evaluation
Mathematical and Computer Modelling: An International Journal
Robust Learning Algorithm Based on Iterative Least Median of Squares
Neural Processing Letters
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The backpropagation (BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. Due to the nonlinear modeling power of such networks, the learned mapping may interpolate all the training points. When erroneous training data are employed, the learned mapping can oscillate badly between data points. In this paper we derive a robust BP learning algorithm that is resistant to the noise effects and is capable of rejecting gross errors during the approximation process. The spirit of this algorithm comes from the pioneering work in robust statistics by Huber and Hampel. Our work is different from that of M-estimators in two aspects: 1) the shape of the objective function changes with the iteration time; and 2) the parametric form of the functional approximator is a nonlinear cascade of affine transformations. In contrast to the conventional BP algorithm, three advantages of the robust BP algorithm are: 1) it approximates an underlying mapping rather than interpolating training samples; 2) it is robust against gross errors; and 3) its rate of convergence is improved since the influence of incorrect samples is gracefully suppressed