Fuzzy evolving linear regression trees

Abstract

This paper introduces a new approach for evolving fuzzy modeling using tree structures. The model is a fuzzy linear regression tree whose topology can be continuously updated through a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. An incremental learning algorithm approach evolves the tree replacing leaves with subtrees that improve the model quality. The learning algorithm evaluates each input only once and do not need to store any past values. The evolving linear regression model is evaluated using time series forecasting problems. The performance is compared against alternative evolving fuzzy models and classic models with fixed structures. The results suggest that fuzzy evolving regression tree is a promising approach for adaptive system modeling.

Geometric approach and structure at infinity controls for the disturbance rejection

“…This approach could also be applied in other kind of systems as are the electric and pneumatic systems. In the forthcoming work, the stability of the controller will be guaranteed and the disturbance will be rejected for the systems, or the disturbance will be considered unknown and will be rejected using the evolving intelligent systems [17][18][19][20][21].…”

Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

“…The FNT has been applied in many areas such as face recognition and microarraybased cancer classification [19]. In comparing models for the non-linear function approximation, it has lower Mean Square Error (MSE) than back-propagation and fuzzy clustering techniques [19][20][21]. The function instruction operators F and instruction terminals T used for evolving a FNT model with instruction set S are described as:…”

Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting

“…Forecasting results for selected data sets are shown to illustrate the performance of the adaptive fuzzy c-regression approach. The results of aFCR are compared with ARIMA, seasonal ARIMA (SARIMA), and with state of the art evolving fuzzy and neuro-fuzzy modeling approaches, DENFIS [10], OS-ELM [26], eTS [17], xTS [37], eTS+ [12], ePL [21], and eFT [25].…”

“…An evolving fuzzy modeling approach using tree structures, namely, evolving fuzzy trees (eFT) was introduced in [25]. The eFT model is a fuzzy linear regression tree whose topology can be continuously updated employing a statistical model selection test.…”