1 research outputs found
Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model
In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or
linear functions are usually utilized as the consequent parts of the fuzzy
rules, but they cannot effectively describe the behavior within local regions
defined by the antecedent parts. In this article, a theoretical and practical
design methodology is developed to address this problem. First, the information
granulation (Fuzzy C-Means) method is applied to capture the structure in the
data and split the input space into subspaces, as well as form the antecedent
parts. Second, the quadratic polynomials (QPs) are employed as the consequent
parts. Compared with constant and linear functions, QPs can describe the
input-output behavior within the local regions (subspaces) by refining the
relationship between input and output variables. However, although QP can
improve the approximation ability of the model, it could lead to the
deterioration of the prediction ability of the model (e.g., overfitting). To
handle this issue, we introduce an exponential weight approach inspired by the
weight function theory encountered in harmonic analysis. More specifically, we
adopt the exponential functions as the targeted penalty terms, which are
equipped with l2 regularization (l2) (i.e., exponential weighted l2, ewl_2) to
match the proposed reinforced second-order fuzzy rule-based model (RSFRM)
properly. The advantage of el 2 compared to ordinary l2 lies in separately
identifying and penalizing different types of polynomial terms in the
coefficient estimation, and its results not only alleviate the overfitting and
prevent the deterioration of generalization ability but also effectively
release the prediction potential of the model.Comment: 22 page