Robust regression and outlier detection
Robust regression and outlier detection
Robust estimation and testing for general nonlinear regression methods
Applied Mathematics and Computation
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm
Data Mining and Knowledge Discovery
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
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Robustness of radial basis functions
Neurocomputing
Robust MCD-Based Backpropagation Learning Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Robust LTS backpropagation learning algorithm
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Fast robust learning algorithm dedicated to LMLS criterion
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Estimation of cure characteristics in rubber extrusion lines
ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
Knowledge discovery in rubber extrusion processes
ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
Data mining and simulation processes as useful tools for industrial processes
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
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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In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model @t-estimates [introduced by Tabatabai, M. A., Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two @j functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.