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By adjusting a Fuzzy ART network parameter, designers can control the similarity of retrieved reference designs.
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The fuzzy logic controller (FLC) assimilates to imperialistic competition by adjusting the fuzzy rules to avoid possible errors of the worst imperialist action selection strategy.
This blueprint structure is then refined by adjusting connections of fuzzy neurons, helping to capture the numeric details of the target system's behavior.
The filter is adjusted by a fuzzy system with two inputs which quantify the unwanted and intentional camera movements.
The smoothing factor of filter, i.e., α(n) is adjusted by a fuzzy system continuously for MC of each frame.
Both temperature regulation and heat flux tracking errors are employed to generate the final control action; their contributions are adaptively adjusted by a fuzzy fusing policy of control actions.
In this article, based on the adaptive interval type-2 fuzzy logic, by adjusting weights, centers and widths of proposed fuzzy neural network (FNN), the modeling errors can be eliminated for a class of SISO time-delay nonlinear systems.
The MCHFLS, minimal configured hierarchical fuzzy logic systems, is minimizing the fuzzy inferences parameters by adjusting essentially the variables associations and levels structures.
To ensure SoC balancing, a gain-scheduling controller based on fuzzy logic is designed in [10] by adjusting the droop coefficient.
The fuzzy controller proposed in this paper provides a simple and robust way to keep the DMFC working at the MPP by adjusting the operating conditions followed by the variation of the driven load in real time.
We propose a learning algorithm from the cost function for adjusting of fuzzy weights.
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