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This paper presents architectures for comparator trees capable of finding the minimum value of a large number of inputs.
The QRA relies on a large number of inputs, many of which were found to be highly uncertain.
Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs.
There are limitations faced by all popular fuzzy systems architectures when they are applied to applications with a large number of inputs (Multi-sensors) systems.
Our method can work with a large number of inputs within reasonable computing time, and can identify both the parameter and the interaction effects.
In this paper, proposes a modified hybrid fuzzy controller for multi-sensors (Large number of inputs) real-time mobile robot navigation.
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In order to account for defocus, existing techniques need to capture a large number of input images.
Numerical simulations are performed for a large number of input data.
The crucial step is to establish proper feature correspondences between a large number of input models.
Another kind of learning remembers a large number of input instances and their correct responses and interpolates between them to deal with new inputs.
This device allows optimization of a large number of input parameters which are critical to interface compliance.
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