Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Regularization theory and neural networks architectures
Neural Computation
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feedforward Neural Network Methodology
Feedforward Neural Network Methodology
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
On Interval Weighted Three-Layer Neural Networks
SS '98 Proceedings of the The 31st Annual Simulation Symposium
Foreword: applications of neural networks
Computers and Operations Research
Artificial neural networks and bankruptcy forecasting: a state of the art
Neural Computing and Applications
Interval computing in neural networks: one layer interval neural networks
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
Forecasting models for interval-valued time series
Neurocomputing
Different Approaches to Forecast Interval Time Series: A Comparison in Finance
Computational Economics
Fuzzy Sets and Systems
Granular data regression with neural networks
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Genetic interval neural networks for granular data regression
Information Sciences: an International Journal
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Interval-valued data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy or variability must be taken into account. In addition, the combination of Interval Analysis with soft-computing methods, such as neural networks, have shown their potential to satisfy the requirements of the decision support systems when tackling complex situations. This paper proposes and analyzes a new model of Multilayer Perceptron based on interval arithmetic that facilitates handling input and output interval data, but where weights and biases are single-valued and not interval-valued. Two applications are considered. The first one shows an interval-valued function approximation model and the second one evaluates the prediction intervals of crisp models fed with interval-valued input data. The approximation capabilities of the proposed model are illustrated by means of its application to the forecasting of daily electricity price intervals. Finally, further research issues are discussed.