Piecewise Modeling Algorithms Using Numerical Data

In this study, a novel piecewise modeling method was devised using numerical data. The piecewise model was represented as a rectangular region divided into a state-space. The vertex values of the rectangular region were determined using the learning algorithm based on a simplified fuzzy inference model because the piecewise model was represented by a fuzzy if-then rule with singleton consequents. The proposed algorithm can be used to determine optimal vertex values and positions of segmented regions and with minimum modeling errors. Three examples were considered to demonstrate the effectiveness of the proposed method using numerical simulations.