A nonlinear transversal fuzzy filter with online clustering

Authors:
Zhengrong Li;Meng Joo Er
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Year:
2003

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Abstract

In this paper, a nonlinear transversal fuzzy filter with online clustering is proposed. It is based on radial-basis-function networks (RBFN) and implements the TSK fuzzy systems functionally. The proposed filter has the following features: (1) Hierarchical structure self-construction. The fuzzy rules, i.e., the RBF neurons are generated automatically in training process. (2) Online clustering. Instead of selecting the centers and widths of membership functions arbitrarily, an online clustering method is applied to ensure the reasonable representation of input terms of an input variable. It not only ensures the proper feature representation, but also optimizes the structure of the filter by reducing the number of fuzzy rules. (3) All free parameters in the premise and consequence parts are online determined by a hybrid sequential algorithm without repeated computation to make real-time applications possible. Using a proposed hybrid learning algorithm, low computation load and less memory requirements are achieved. Simulation results show that the proposed filter can obtain better or same accuracy with lower system resource requirements compared with other similar approaches.