Making large-scale support vector machine learning practical
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Black-box modeling of a complex industrial process
ECBS'99 Proceedings of the 1999 IEEE conference on Engineering of computer-based systems
Training neural networks with additive noise in the desired signal
IEEE Transactions on Neural Networks
Learning neural networks with noisy inputs using the errors-in-variables approach
IEEE Transactions on Neural Networks
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In industry there are many complex modeling tasks where the most of the available information is in the form of input-output data. In such cases only black box modeling can be used, where the model can be built using learning methods. In black-box modeling one of the most important tasks is to obtain good training data. However, in most real world problems the available data are imprecise, contain noise or some distortion. This paper discusses some problems of neural model building based on noisy training data. Two methods - the errors-in-variables training method (EIV) and the support vector machines (SVM)- are introduced and compared to the performance of the traditional neural network solution. The performance of the SVM method is also tested on a real industrial problem, namely on the modeling of a Linz-Donawitz steel converter.