Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conven...

The identification of nonlinear multi-input multi-output (MIMO) processes is important and challenging problem. Fuzzy systems have been effectively used to identify complex nonlinear dynamical systems, but mostly single-input single-output systems are considered. This paper presents a compact Takagi-Sugeno fuzzy model that can be effectively used to represent MIMO dynamical systems. For the ide...

Inverse fuzzy process model based direct adaptive control. [2] J. Abonyi and R. Babuška. Local and global identification and interpretation of parameters in Takagi–Sugeno fuzzy models. In Proceed-[3] J. Abonyi and R. Babuška. Local and global identification and interpretation of parameters in Takagi–Sugeno fuzzy models. In Proceed-tification and control of nonlinear systems using fuzzy Hammerst...

The adaptive fuzzy and fuzzy neural models are being widely used for identification of dynamic systems. This paper describes different fuzzy logic and neural fuzzy models. The robustness of models has further been checked by Simulink implementation of the models with application to the problem of system identification. The approach is to identify the system by minimizing the cost function using...

We propose a fuzzy-neural modeling approach for automatically constructing a fuzzy-neural model from a set of input-output data. The proposed approach consists of two phases, structure identification and parameter identification. In the structure identification phase, rough TSK fuzzy rules are extracted through a clustering algorithm. Then a fuzzy neural network is built in the parameter identi...

the purpose of this study was to estimate the torque from high‑density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate‑to‑high isometric elbow flexion‑extension. the elbow torque was estimated in two following steps: first, surface electromyography (emg) amplitudes were estimated using principal com...

-Truck backer-upper problem, considered an acknowledged benchmark in nonlinear system identification, is an excellent test-bed for fuzzy control systems. Fuzzy controller, formulated on the basis of human understanding of the process or identified from measured control actions, can be regarded as an emulator of human operator. Controller design, however, may become difficult, especially if the ...

since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...

This paper presents a new approach to acoustic noise identification, by introducing fuzzy modeling techniques. Fuzzy identification is compared to conventional linear identification techniques, using as the system to model a new device called ElectroMechanical Film (EMF), developed by VTT [1]. This device can be used either as an acoustic sensor or actuator. The obtained model represents the be...

Performance of a fuzzy system identifier is investigated against a fossil fuel boiler data. A multi-layer neuro-ftrzzy system presents identification of a drum type boiler. This identification techruque provides a rule-based approach to express the boiler dynamics in fuzzy rules that are generated from the experimental boiler data. The interconnections of neuro-fuzzy layers furnish these fuzzy ...