Fast learning method of interval type-2 fuzzy neural networks

The Fuzzy Set Parameter Estimation algorithm is proposed for fast learning interval type-2 fuzzy neural networks applied for classification problems. Classes are disjoint. Learning consists of estimating appropriate values of fuzzy set parameters in every rule. Estimation is based on statistical properties of the training data. The experimental study confirms that it is dozens times quicker than the backpropagation method, while the classification effectiveness is comparable.