Fuzzy Model Identification Research Articles

The Application of a New Fuzzy Model Identification Technique to a Human Tracking Control Operator

A concise statement of a recursive fuzzy estimator and probabilistic fuzzy identifier is presented as elements of a systematic fuzzy modelling technique capable of emulating a prototype dynamic system for which only the input—output behaviour is given. The application of these techniques to derive a fuzzy model of a human operator as controller in a tracking loop is illustrated. A number of fuzzy analysis tools are used in order to investigate the resultant fuzzy model mechanisms.

Empirical comparison of methods of fuzzy relational identification

A number of methods have been proposed for the identification and self learning of relational fuzzy models. The paper compares some of the methods and looks at their tolerance to noise and to the choice of initial fuzzy ranges. Data from runs of a simulated fed-batch fermenter are used as a test case. The results show that identified relational models can give as good results as rule-based models and can be made to be very tolerant of noise in the identification data.

Structure identification of fuzzy model

The problems of structure identification of a fuzzy model are formulated. A criterion for the verification of a structure is discussed. Using the criterion, an algorithm for identifying a structure is suggested. Further, a successive identification algorithm of the parameters is suggested. The proposed methods are applied to an example.

Fuzzy Model Identification and Self-Learning for Dynamic Systems

The algorithms of fuzzy model identification and self-learning for multi-input/multi-output dynamic systems are proposed. The required computer capacity and time for implementing the proposed algorithms and related resulting models are-significantly reduced by introducing the concept of the referential fuzzy sets. Two numerical examples are given to show that the proposed algorithms can provide the fuzzy models with satisfactory accuracy.