Efficient data-driven modeling with fuzzy relational rule network

An algorithmic approach for efficient identification of a fuzzy relational rule network (FR/sup 2/N) from data is presented. FR/sup 2/N uses a relational input partition for human-understandable modeling of linear interactions between the input variables. Mutual subsethood has been used to estimate the optimal interaction structure. An analytical relationship between the mutual subsethood measure and one of the parameters of the membership functions is derived. The use of this relationship results in a dramatic speed-up of the identification process.