Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
TAO-robust backpropagation learning algorithm
Neural Networks
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Annealing robust fuzzy basis function for modelling with noise and outliers
International Journal of Computer Applications in Technology
Robust MCD-Based Backpropagation Learning Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
A Robust Support Vector Regression Based on Fuzzy Clustering
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
ARFNNs with SVR for prediction of chaotic time series with outliers
Expert Systems with Applications: An International Journal
Robust LTS backpropagation learning algorithm
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
Outlier identify based on BP neural network in dam safety monitoring
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
Fast robust learning algorithm dedicated to LMLS criterion
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Outliers detection in environmental monitoring databases
Engineering Applications of Artificial Intelligence
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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
Robust Learning Algorithm Based on Iterative Least Median of Squares
Neural Processing Letters
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Multilayer feedforward neural networks are often referred to as universal approximators. Nevertheless, if the used training data are corrupted by large noise, such as outliers, traditional backpropagation learning schemes may not always come up with acceptable performance. Even though various robust learning algorithms have been proposed in the literature, those approaches still suffer from the initialization problem. In those robust learning algorithms, the so-called M-estimator is employed. For the M-estimation type of learning algorithms, the loss function is used to play the role in discriminating against outliers from the majority by degrading the effects of those outliers in learning. However, the loss function used in those algorithms may not correctly discriminate against those outliers. In the paper, the annealing robust backpropagation learning algorithm (ARBP) that adopts the annealing concept into the robust learning algorithms is proposed to deal with the problem of modeling under the existence of outliers. The proposed algorithm has been employed in various examples. Those results all demonstrated the superiority over other robust learning algorithms independent of outliers. In the paper, not only is the annealing concept adopted into the robust learning algorithms but also the annealing schedule k/t was found experimentally to achieve the best performance among other annealing schedules, where k is a constant and t is the epoch number