This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables an...

This paper addresses the interpretation of parameters in Takagi-Sugeno (TS) fuzzy models. The analysis is presented for the dynamic gain and steady-state representation, but it holds for parameters related to the dynamics as well. The TS model interpolates between local linear models. The overall gain obtained by interpolating the gains of the local models can be interpreted as the local dynami...

Function al reasoning or the Takagi-Sugeno-Kang model is a fuzzy reasoning method aiming at numerical accuracy and has found wide use in fuzzy modeling. ln this method, each rule consists of a fuzzy implication and a functional consequence part. ln this work, a new, online identification method for such a system is presented, for supervised learning tasks. Structure identification is executed b...

Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternati...

In this paper, a type of compensation-based recurrent fuzzy neural network (CRFNN) for identifying dynamic systems is proposed. The proposed CRFNN uses a compensation-based fuzzy reasoning method, and has feedback connections added in the rule layer of the CRFNN. The compensation-based fuzzy reasoning method can make the fuzzy logic system more adaptive and effective, and the additional feedbac...

In this paper, an approach for storing and retrieving the past data by means of a novel Fuzzy Data Window Memory (FDWM) is reported. The data, which is selected for memorization, is based on the highest firing strength of the fuzzy rule. The size of the proposed FDWM is much smaller than traditional window memory with no degradation in performance. A computer simulation study of one of the appl...

This paper describes a novel nonlinear modeling approach by on-line clustering, fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upper bounds of t...

Fuzzy model-based adaptive control, unlike traditional fuzzy control, extracts expert knowledge from data by using model identification techniques. In this paper, we propose an analysis of the contradiction in this knowledge as an additional search criterion during the model identification process. In order to do so, we first define a measure of contradiction between fuzzy rules. Then, we assum...

Fuzzy model-based adaptive control, unlike traditional fuzzy control, extracts expert knowledge from data by using model identification techniques. In this paper, we propose an analysis of the contradiction in this knowledge as an additional search criterion during the model identification process. In order to do so, we first define a measure of contradiction between fuzzy rules. Then, we assum...

The integration of fuzzy systems and neural networks has recently become a popular approach in engineering fields for modelling and control of uncertain systems. This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization of timevailable online 2 May 2010 eywords: ype-2 fuzzy systems euro-fuzzy network dentification varying ...