An Input-Output Clustering Method for Fuzzy System Identification

Clustering algorithms are often used for fuzzy system identification. However, most clustering algorithms do not consider the outputs for clustering. In addition, these algorithms do not consider how to obtain the optimal number of clusters. Without the optimal number of clusters, the final set of clusters may be inappropriate. To address this, this paper presents an Input-Output Clustering (IOC) algorithm to determine both the correct number of clusters and the appropriate location for them by considering both inputs and outputs. The proposed algorithm, when used for fuzzy system identification, achieves better performance than existing clustering methods. This performance is illustrated by examples of function approximation and dynamic system identification.