Abstract
This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct 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). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules.
Highlights
In a similar way fuzzy clustering has been utilized as well in classifying data-driven fuzzy modeling, since it draws a methodology for assigning label to similar data
This article has concentrated on the modeling of nonlinear dynamic systems via the utilization of the well known fuzzy modeling paradigm, the Takagi-Sugeno (T-S) technique
T-S models depend heavily on some initial membership centers of the universe of discourse of used fuzzy variables, such centers have been obtained by employing clustering algorithm
Summary
In a similar way fuzzy clustering has been utilized as well in classifying data-driven fuzzy modeling, since it draws a methodology for assigning label to similar data. Differing from existing clustering-based methods, in their approach the structure identification of the fuzzy model, including input selecting and partition validating, was implemented on the basis of a class of sub-clusters created by a self-organizing network instead of raw data. The projection of the prototypes and variables of clusters was a recognized approach to extracting the information included in the data clusters into fuzzy sets Merging these fuzzy sets, based on proposed guidelines, can minimize the number of rules and make the identifying control strategy more transparent. Bossley (1997) has looked into the problem of antenna modeling via neuro-fuzzy systems, getting an optimized five layers neural network was not achieved due to the large number of generated fuzzy rules. Sets of such nature were removed from the antecedent of the rules, reducing the number of the fuzzy rules
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