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An application of interval-valued neural networks to a regression problem. (English) Zbl 1149.92301
Summary: This paper is concerned with exploiting uncertainty in order to develop a robust regression algorithm for a pre-sliding friction process based on a Nonlinear Auto-Regressive with eXogenous inputs neural network. Essentially, it is shown that using an interval-valued neural network allows a trade-off between the model error and the interval width of the network weights or a ‘degree of uncertainty’parameter. The neural network weights are replaced by interval variables and cannot therefore be derived from a conventional optimization algorithm; in this case, the problem is solved by using differential evolution. The paper also shows how to implement the idea of ‘opportunity’as used in Ben-Haim’s information-gap theory.

MSC:
92B20 Neural networks for/in biological studies, artificial life and related topics
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