A fuzzy projection pursuit model for prediction in high-dimensional space with fuzzily dependent inputs

In this paper, we propose a new fuzzy system architecture based on the idea of projection pursuit. This new model is suitable for the cases where the input variables are related to each other but their relationships cannot be precisely defined. The structure of the model is determined by projecting the sampling data onto lower and lower dimensional spaces in such a way that the rules automatically focus on the regions crowded with sampling data. The parameters in the model are estimated as the weighted average of the sampling data over the fuzzy region the parameter is responsible. The main advantages of this fuzzy projection pursuit model include its flexibility in searching relationships among fuzzily dependent input variables, its ability to deal with outliers in the data, and the ease of interpretation of its structure and parameters