iMLP: Applying Multi-Layer Perceptrons to Interval-Valued Data
Neural Processing Letters
Forecasting models for interval-valued time series
Neurocomputing
Interval computing in neural networks: one layer interval neural networks
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Numerical methods of interval analysis in learning neural network
Automation and Remote Control
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In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, we have interval neural networks. By analyzing the mathematical model, we categorize general three-layer neural network training problems into two types. One of them can be solved by finding numerical solutions of nonlinear systems of equations. The other can be transformed into nonlinear optimization problems. Reliable interval algorithms such as interval Newton/generalized bisection method and interval branch-and-bound algorithm are applied to obtain optimal weights for interval neural networks. The applicable state-of-art interval software packages are reviewed in this paper as well.