Local Linear Neuro-Fuzzy Models: Fundamentals

This chapter deals with local linear neuro-fuzzy models, also referred to as Takagi-Sugeno fuzzy models, and appropriate algorithms for their identification from data. The local linear modeling approach is based on a divide-andconquer strategy. A complex modeling problem is divided into a number of smaller and thus simpler subproblems, which are solved (almost) independently by identifying simple, e.g., linear, models. The most important factor for the success of such an approach is the division strategy for the original complex problem. Therefore, the properties of local linear neuro-fuzzy models crucially depend on the applied construction algorithm that implements a certain division strategy. This chapter focuses on the local linear model tree (LOLIMOT) algorithm proposed by Nelles [267, 271, 286].