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For the water model, the errors are smaller than 0.04 for all parameters and station configurations.
We model the errors of ϑ ̂ b - 1 as zero-mean Gaussian with error covariance matrix Pb-1.
In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them.
To model the errors generated by the transmission system, let us consider the binary symmetric channel (BSC) with the error probability, P e, and denote by e(D) the error pattern and by y(D) the received sequence so that: y ( D ) = c ( D ) + e ( D ) (5).
For water surface prediction, the MAE, RMSE and δ errors decreased with the DT-MLP model by 5.2%, 5.5% and 0.095% compared with MLP and with the DT-RBF model the errors decreased by 20%, 23% and 0.5% compared with RBF, respectively.
In this model, the errors are assumed by a first-order autoregressive process.
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For the MB-SW model the error is consequently (29d).
Moreover, in an error correction model, the error correction term is significant.
In the ordered logit model the error term is assumed to be logistically distributed, whereas in the ordered probit model the error term is normally distributed.
Three-dimensional numerical results demonstrate the effectiveness of the coupled model, the error estimates, and the adaptive modeling procedure.
Then, in Section 3 we model the error caused by the symbol level limiter in the transmitted signal.
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