A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplifying neural networks by soft weight-sharing
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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In this paper a new method of parameters estimation for neuro-fuzzy system with parameterized consequents is presented. The novelty of the learning algorithm consists of an application of the deterministic annealing method integrated with ε-insensitive learning. This method allows to improve neuro-fuzzy modeling quality in the sense of an increase in generalization ability and outliers robustness. To demonstrate performance of the proposed procedure two numerical experiments concerning benchmark problems of prediction and identification are given.