Trade-off between accuracy and interpretability: Experience-oriented fuzzy modeling via reduced-set vectors

This paper focuses on accuracy and interpretability issue of fuzzy model approaches. In order to balance the trade-off between both of the aspects, a new fuzzy model based on experience-oriented learning algorithm is proposed. Firstly, support vector regression (SVR) with presented Mercer kernels is employed to generate the initial fuzzy model and the available experience on the training data. Secondly, a bottom-up simplification algorithm is introduced to generate reduced-set vectors for simplifying the structure of the initial fuzzy model, at the same time the parameters of the simplified model derived are adjusted by a hybrid learning algorithm including linear ridge regression algorithm and gradient descent method based on a new performance measure. Finally, taking the results from two-dimensional sinc function approximation and fuzzy control of the bar and beam system, the proposed fuzzy model preserves nice accuracy and interpretability.