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Abstract
Fuzzy logic systems (or, simply, fuzzy systems, FSs) and neural networks are universal approximators, that is, they can approximate any nonlinear function (mapping) with any desired accuracy, and have found wide application in the identification, planning, and model-free control of complex nonlinear systems, such as robotic systems and industrial processes. Fuzzy logic offers a linguistic (approximate) way of drawing conclusions from uncertain data, and neural networks offer the capability of learning and training with or without a teacher (supervisor).
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