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By tuning an error tolerance parameter, a trade-off between performance and convergence time can also be achieved.
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The performance of the designed closed-loop system is tuned by an error feedback.
By tuning the code rate, a tradeoff of bit rate and bit error probability can be achieved.
The mathematical formulation consists of an ANN with an unsupervised error, which is minimized by tuning weights of the network.
The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator.
In this section, we show how the error rate can be controlled by tuning the threshold resistance Rth 00,01) for a specific data storage time.
The measurement errors are smoothed by tuning the parameters of the Kalman filter to achieve a balance between the responsiveness of the tracker and estimate variance.
These analyses show that the tracking errors are incurred by such a shift and improved by tuning the rectifying gain.
By proposing a novel dynamic velocity observer, an adaptive output feedback consensus algorithm is proposed such that the tracking errors of all agents can converge to an arbitrarily small neighborhood of zero by tuning the design parameters.
Model errors can be reduced by roughly 50% by tuning γ for each data record.
However, one might be able to balance the effect of time-/state-varying delay distributions by tuning the reaction rate of the dominant delayed reaction, and this can even be done in a simple 'trial and error' manner.
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