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Analysis of universal-logics-based fuzzy neural networks

To improve the inference performance of fuzzy neural networks (FNN), this paper presents a new type of FNN where the Universal Logics are employed, which makes it possible to enhance the performance of FNN not only by tuning the shapes of the membership functions, but also by tuning the parameters of the fuzzy inference structure. Finally, the simulation result in process control proves its effectiveness.