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Data analysis was performed using the Q-Gene program (Equation 2) [ 28].> All statistical analyses were performed using Prism 5 (Graphpad Software).
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The optimal control law is derived from the dynamical programming equation and control constraints without solving the dynamical programming equation.
Then, the stochastic dynamical programming principle is applied to the partially averaged Itô equations to establish a dynamical programming equation.
Then the dynamical programming equation is established by applying the stochastic dynamical programming principle to the control problem.
Then applying the stochastic dynamical programming principle to the controlled diffusion processes yields a dynamical programming equation, taking into account random excitation spectra.
Then, the dynamical programming equation for the control problems of minimizing the response of the averaged system is formulated based on the dynamical programming principle.
The quasi-linear equations for system states are derived from the model and the dynamical programming equation for the system is obtained.
The technique involves the following new elements: similarity scoring functions, a dynamic programming equation, a back tracking procedure, and an alignment generation method.
It is shown that the optimal maintenance decisions related to this problem are a solution of a continuous-time renewal-type dynamic programming equation.
Then, the dynamical programming equation for the control problem of minimizing response of the systems is formulated from the partially completed averaged Itô equations by using the dynamical programming principle.
Then, the dynamical programming equation for non-linear stochastic optimal control of the system is derived from the averaged Itô equations by using the stochastic dynamical programming principle and solved to yield the optimal control law.
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CEO of Professional Science Editing for Scientists @ prosciediting.com