A new method for hybrid-fuzzy identification

Abstract:

In this paper a new identification method for non-linear hybrid systems that have mixed continuous and discrete states by using fuzzy clustering and principal component analysis is described. The method first determines the hybrid characteristic of the system inspired by an inverse form of the merge method for clusters, which makes it possible to identify the unknown switching points of a process based on just input-output data. Using the switching points, a hard partition of the input-output space is obtained. Then, we propose to use Takagi-Sugeno (TS) fuzzy models with Gaussian MFs as sub-models for each partition. Thus, the overall model is hybrid-fuzzy and will include explicitly the hybrid behavior of the system (the detected switching points) by means of binary MFs, and in each partition all the other non-linearities by means of TS sub-models. An illustrative experiment on a hybrid-tank system is conducted to present the benefits of the proposed approach. © 2011 IFAC.

Año de publicación:

2011

Keywords:

• Nonlinear system identification
• Principal Component Analysis
• Hybrid and distributed system identification
• Fuzzy Clustering
• fuzzy identification

Fuente:

scopus