Redefined Fuzzy Min-Max Neural Network

The classical fuzzy min-max (FMM) neural network easy to cause the overlap of hyperboxes from different classes, which affect the pattern classification performance. In this paper, we propose a redefined fuzzy min-max (RFMM) neural network to solve this problem. The main contribution is to modify the basic architecture of FMM by adding a redefined hyperbox layer. The proposed RFMM is a four-layer feedforward neural network. The generated hyperbox layer and the redefined hyperbox layer are connected through the proposed hyperbox filter, hyperbox optimization and hyperbox combination. The RFMM learning algorithm is an expansion/contraction/redefinition process. The effectiveness of RFMM is evaluated based on ten benchmarks. Experimental results indicate that RFMM leads to better classification performance than various FMM-based, support vector machine-based models and lower sensitivity to the maximum size of expansion coefficient.