E.A. Al-Gallaf (Bahrain)
: Neuro-Fuzzy Systems, Fuzzy Clustering, Takagi-Sugeno Modeling, Nonlinear Systems.
This article investigates the application of a Neuro-fuzzy system to nonlinear dynamic system modeling from a set of input-output training patterns. It is concentrated on the modeling via TAKAGI-SUGENO (T-S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Created initial memberships are then employed to construct the suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). Compared to other well-known approximation techniques such as artificial neural networks, fuzzy systems provide a more transparent representation of the system under study, which is mainly due to the possible linguistic interpretation in the form of rules. Such intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of fuzzy if-then rules. The T-S Fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs.
Important Links:
Go Back
