A practical Bayesian framework for backpropagation networks
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Fuzzy Control: Synthesis and Analysis
Fuzzy Control: Synthesis and Analysis
A neuro-fuzzy framework for predicting ash properties in combustion processes
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we describe a method to derive prediction intervals for neuro-fuzzy networks used as predictive systems. The method also enables the definition of prediction intervals for the fuzzy rules that constitute the rule base of the neuro-fuzzy network, resulting in a more readable and robust knowledge base. Moreover, the method does not depend on a specific architecture and can be applied to a variety of neuro-fuzzy models. An illustrative example and a real-world case study are reported to show the effectiveness of the proposed method.