Interpretability improvement of RBF-based neurofuzzy systems using regularized learning

Radial-basis-function (RBF) networks are mathematically equivalent to a class of fuzzy systems under mild conditions. Therefore, RBF networks have widely been used in learning of neurofuzzy systems to improve the performance. However, in most cases, the interpretability of fuzzy system will get lost after neural network learning. This chapter proposes a learning method using interpretability based regularization for neurofuzzy systems. This method can either be used in extracting interpretable fuzzy rules from RBF networks or in improving the interpretability of RBF-based neurofuzzy systems. Two simulation examples are presented to show the effectiveness of the proposed method.