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In medicine, expert systems using fuzzy inference can help doctors diagnose diabetes and prostate cancer.
A novel control method for a general class of nonlinear systems using fuzzy logic systems (FLSs) is presertted.
In this paper an approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented.
We describe in this paper a new approach for automated mathematical modelling and simulation of robotic dynamic systems using fuzzy logic techniques, genetic algorithms and fractal theory.
This paper presents a new adaptive terminal sliding mode tracking control design for a class of nonlinear systems using fuzzy logic system.
This paper addresses the problem of adaptive tracking control for a class of stochastic strict-feedback nonlinear time-varying delays systems using fuzzy logic systems (FLS).
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Some expert systems use fuzzy logic.
The authors propose algorithm for the automatic collision avoidance system using fuzzy inference in this paper.
To determine the degree of deprivation, we then develop a new system using fuzzy logic.
A fuzzy-based FMEA is introduced to express such uncertainties appropriately and to provide flexible access to a risk picture for a new system using fuzzy modeling.
This research develops a balancing approach for a novel SAMS model, called the cart seesaw system, using fuzzy logic and fuzzy coordinator compensation to drive the sliding carts and keep the seesaw angle close to zero in the equilibrium state.
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