TAO-robust backpropagation learning algorithm
Neural Networks
Similarity based method for manufacturing process performance prediction and diagnosis
Computers in Industry
Prediction of MPEG video source traffic using bilinear recurrent neural networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Expert Systems with Applications: An International Journal
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: An International Journal
Neural network based modelling of environmental variables: A systematic approach
Mathematical and Computer Modelling: An International Journal
A Performance evaluation of neural network models in traffic volume forecasting
Mathematical and Computer Modelling: An International Journal
Several methods usable in production systems prediction
Mathematical and Computer Modelling: An International Journal
Wireless Personal Communications: An International Journal
Information Sciences: an International Journal
Analytical approach to similarity-based prediction of manufacturing system performance
Computers in Industry
Proceedings of the Fourth Symposium on Information and Communication Technology
Neural network ensemble operators for time series forecasting
Expert Systems with Applications: An International Journal
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We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series