# mean square

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## mean square

[¦mēn ′skwer]
(statistics)
The arithmetic mean of the squares of the differences of a set of values from some given value.
References in periodicals archive ?
The air velocity root mean square, in the exit air terminal devices, is higher in test II than in test I, while in the turbulence intensity the opposite is verified.
The Least mean square algorithm changes the filter tap weight so that the error e(k) is minimized in the mean square sense.
The stochastic cocycle [PHI]: [R.sub.+] X [OMEGA] [right arrow] B(X) is said to be exponentially stable in mean square if there exist two positive constants N, v such that
Using our main theorem from below one can establish the same mean square convergence rate [[gamma].sub.2] = 0.5 for systems (1.1) satisfying the more general conditions (1.2)-(1.5), even for dissipative systems of SDEs on infinite intervals [0,+[infinity]].
The naive model produces forecasts having mean square error of .109 and mean absolute error of .266; the corresponding values given by Eskew are .145 and .2995 (Eskew 1979, Table 4, 115).
Then, the models were fitted according to the number of input variables (3 and 4 inputs), number of layers (1, 2 and 3 layers) and transfer functions (Polynomial, Sigmoid, RBF and Tangent); in this case, the best fitted model was considered as that with lowest mean square error (MSE) of the predicted values.
Results Train Test Overall MSE 79671.423 253835.159 114918.846 RMSE 282.261 503.821 338.997 NRMSE 0.045 40.908 18.520 MAE 196.618 352.406 228.146 MAPE 47.503 136.486 65.512 MSE: mean square error; RMSE: root mean square error; NRMSE: normalized root mean square error; MAE: mean absolute error; MAPE: mean absolute percentage error
Under such conditions, the environmental factor was significant and showed a significant mean square (283875748), which is considered the greatest magnitude among all the main effects of the analysis of variance (Table 1).
The presented artificial neural network was trained using the Levenberg-Marquardt algorithm and the mean square error was used as a performance function .
In each band, the high protruding frequency is extracted as the feature, and root mean square (RMS) value for these protruding frequencies are used for fault detection.
Mean square error, normalized cross-correlation, and structural similarity were used as evaluation indexes to verify the superiority of this method.
Necessary and sufficient conditions for the system to achieve mean square bipartite consensus based on event-triggered protocols are given.

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