Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Universal approximation using radial-basis-function networks
Neural Computation
A procedure for the detection of anomalous input-output patterns
Intelligent Data Analysis
| Hi-index | 0.00 |
This paper presents a method to estimate the reliability of the output of a (possibly neuro-fuzzy) model by means of an additional neural network. The proposed technique is most effective when the reliability of the model significantly varies in different areas of input space, as it often happens in many real-world problems, allowing the user to predict how reliable is a given model for each specific situation. Alternatively, the proposed technique can be used to analyze particular anomalies of input data set such as the outliers.