Structure Trade-off Strategy for Local Model Networks

For tree-based partitioning algorithms that lead to Takagi-Sugeno fuzzy models the optimization of the consequents part goes hand in hand with the optimization of the premises part. The prediction performance can significantly be improved with the application of subset selection methods for the local polynomial models. Traditionally, the subset selection methods are applied after the model structure is already optimized. The main idea and new contribution of this work is the implementation of a so called structure trade-off procedure which allows to automatically find a good compromise between the number of local models and the flexibility of the local polynomial rule consequents. The main innovation of this approach is that the structure optimization and variable selection can be performed at the same time.