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]].

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

The presented artificial neural network was trained using the Levenberg-Marquardt algorithm and the

mean square error was used as a performance function [26].

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.