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In this paper, we propose to use minimum error power method [24] to estimate the prediction coefficients by computing the minimum error power.
For different order values, we can get different minimum error power values.
The order P + Q and the prediction coefficients are computed by referring to the minimum error power method.
After the prediction coefficients A K are estimated, the minimum error power value with the order K can be computed by referring to (6).
Among all the minimum error power values, the smallest one is corresponding to the order and the prediction coefficients used for reversible data hiding.
The optimal prediction order can be chosen according to the complexity of an audio file by using minimum error power method. .
Similar(50)
Finally, a set of solutions that represents the sound power of noise sources can be found and these solutions have minimum error in least squares sense.
"Employees can pick out things quickly, with minimum error".
The green dot indicates minimum error.
These learning curves are usually well characterized by inverse power-laws:
Details of minimum error predictors for all samples and subsets.
More suggestions(15)
minimum error excitation
minimum transmit power
minimum error value
minimum error threshold
minimum error covariance
minimum carrier power
minimum error requirement
minimum error correction
minimum excitation power
minimum target power
minimum error margin
minimum reactive power
minimum error probability
minimum sound power
minimum actuation power
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