The target input is varied between its maximum and minimum value while all other inputs are fixed, and the outputs of varied target input is computed.
The sound power generated by the shell under a harmonic force input is computed with a lumped parameter/wave superposition method.
During only one experiment, an optimal time-varying input is computed to optimize a criterion, while the unknown model parameters are estimated at the same time.
The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem.
For simplicity of illustration, the state transitions from x (k ) = [ 0 0 0 ] T, [ 0 0 1 ] T, [ 0 1 0 ] T, [ 1 1 0 ] T are illustrated in Figure 2. In the existing solution methods for optimal control of PBNs, the optimal control input is computed using dynamic programming with state transition diagrams.
The Power consumption, Delay, Garbage outputs and Constant inputs are computed.
As part of this analysis, the amplitudes of the cable vibration response to support inputs is computed.
Afterward, regressions of these inputs are computed and the best ones are extracted to form the first layer.
Hence, the cross modal inputs are computed as (14) c i v (t ) = ∑ j W v a · y i j a (t ), c i j a (t ) = W a v · y i v (t ).
On the one hand, with the exception of SCG, the conjugate gradient algorithms use linear searches at each iteration and thus, are computationally expensive as they require that the network response to all training inputs be computed several times for each search.
