Fuzzy Logic Controllers (FLCs) are effective solutions for nonlinear and parameter variability systems, but it contains multiple mathematical operations causing the controller to react slowly. This study aims to obtain a controller that can imitate the effective control performance of the FLC, which is easy to design both in software and hardware, and has a short response time. Artificial neural networks (ANNs) provide effective solutions in system modeling. Modeling of FLC has been realized by using of ANN's learning and parallel processing capability. The design process of the FLC and the training processes of the ANN were studied in Matlab SIMULINK environment. In the study, FLC was modelled at high similarity ratio with small ANN structure. ANN results were obtained very faster than the FLC control performance. The control performances of two controllers were observed to be very close to each other. As a result, ANN model has smaller structure than FLC, which makes it possible to implement the controller easily in terms of hardware and software.

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