Fuzzy logic sliding mode controller based solar PV fed UPQC for improvement of dynamic performance and power quality enhancement in distribution power system. [PDF]
Sravanthi G, Rosalina KM, Reddy TRS.
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CSQUiD: an index and non-probability framework for constrained skyline query processing over uncertain data. [PDF]
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Improving the functionality of wireless sensor networks through the use of reinforcement learning and metaheuristic based energy efficient system. [PDF]
Zhang S, Liu X.
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IoT assisted fuzzy inference systems for intelligent 3D art design in movie animation scene design. [PDF]
Shao Z.
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Enhancing explainability in epidemiological predictions using fuzzy logic integrated with machine and deep learning algorithms. [PDF]
Fatima U, Khushal R.
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Machine learning-assisted computation of the solubility of solid drugs in supercritical carbon dioxide. [PDF]
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Transfer learning with fuzzy decision support for multi-class lung disease classification: performance analysis of pre-trained CNN models. [PDF]
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Optimized fault detection and control for enhanced reliability and efficiency in DC microgrids. [PDF]
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Climbing Mechanism Design and Fuzzy PID-Based Control for a Stay Cable De-Icing Robot. [PDF]
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Related searches:
A defuzzification method respecting the fuzzification
Fuzzy Sets and Systems, 1997Abstract When designing a fuzzy controller, the first thing to do is to choose input and output fuzzifications. The next step consists of building the fuzzy rules table describing the behaviour of the controller. Finally, in order to convert the fuzzy output in a usable form, we need to select a defuzzification strategy.
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