PROJECT TITLE :

Receding Horizon Based Feedback Optimization for Mix-Valued Logical Networks

ABSTRACT:

The optimization of mix-valued probabilistic logical networks may be a natural extension of optimization of Boolean networks. During this study we have initial obtained a recursive answer for the finite horizon case. Then we have proved that when the filter length is massive enough, the obtained optimal management sequence coincides with the one for the infinite horizon case using the reeding horizon technique. This result turns looking out an infinite sequence of controls into finding an optimal feedback matrix by solving a finite horizon optimization drawback. As examples, its applications to human-machine game and to metastatic melanoma are investigated.


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