Topic
Membership function
About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.
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TL;DR: Two estimation methods along with a fuzzy least-squares approach are proposed that can be effectively used for the parameter estimation of fuzzy regression models.
107 citations
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TL;DR: The proposed approach demonstrates the potential for formalizing the inclusion of learning effects into the LOB scheduling of repetitive-unit construction by incorporating relevant factors such as, number of operations in one unit, activity complexity, and job and management conditions.
107 citations
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03 Jun 2006TL;DR: A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance using a set of alternate membership function centers that guides the adaptation process in order to prevent drift.
Abstract: A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Techniques traditionally used in adaptive control for robust parameter updates may not be sufficient for fuzzy schemes. In particular, e-modification may result in the fuzzy-membership centers drifting to large values when persistent oscillations are present in the input. These large values can cause control signal chatter, which can be undesirable or even cause instability if they excite unmodeled dynamics. A new method for robust updates is proposed that prevents this drift in the fuzzy membership centers. In the new method, a set of alternate membership function centers guides the adaptation process in order to prevent drift. A Lyapunov-stability proof ensures the uniform ultimate boundedness of all signals. A simulation of a quadrotor helicopter demonstrates the high performance and robust stability of the new method.
107 citations
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TL;DR: Results of empirical research are presented which focused on the problem of modelling vagueness, i.e. determining membership functions of fuzzy sets which are considered as quantitative representations of vague concepts such as ‘young man’, ‘long sticks’), etc.
107 citations
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TL;DR: Experimental results obtained from various samples show that the present method is insensitive to Gaussian and impulsive noises and able to improve the quality of the image by focusing to the appropriate target object.
Abstract: A new passive autofocus algorithm consisting of a robust focus measure for object detection and fuzzy reasoning for target selection is presented. The proposed algorithm first detects objects distributed in the image using a mid-frequency discrete cosine transform focus measure and then selects the target object through fuzzy reasoning with three fuzzy membership functions. The proposed algorithm is designed as full digital blocks and fabricated using 0.35-mum CMOS technology. Experimental results obtained from various samples show that the present method is insensitive to Gaussian and impulsive noises and able to improve the quality of the image by focusing to the appropriate target object.
107 citations