Multilayer feedforward networks are universal approximators
Neural Networks
Neural network design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
TAO-robust backpropagation learning algorithm
Neural Networks
Robust MCD-Based Backpropagation Learning Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Robust neural network for novelty detection on data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Robust neural network learning algorithms are often applied to deal with the problem of gross errors and outliers. Unfortunately, such methods suffer from high computational complexity, which makes them ineffective. In this paper, we propose a new robust learning algorithm based on the LMLS (Least Mean Log Squares) error criterion. It can be considered, as a good trade-off between robustness to outliers and learning efficiency. As it was experimentally demonstrated, the novel method is not only faster but also more robust than the LMLS algorithm. Results of implementation and simulation of nets trained with the new algorithm, the traditional backpropagation (BP) algorithm and robust LMLS method are presented and compared.