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Książka = Book ; KS/2/1992/R25
Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences
197-206 pages ; 21 cm ; Bibliography p. 205-206
We believe that an essential feature in machine learning is the real time satisfaction of multiple objectives such as identification, tracking etc. The machine learning problem may be viewed as a nonlinear adaptive control problem where the environment plays the role of the 'plant', while the learner is the controller. Multiobjective optimization (MOO) in the control problem typically deals w1th simultaneous optrmlzatton of more than one objecttve, where cach objecttve is descnbed via a cost functional. In sucha situation there of ten exists a region of tradeoff wherein one cost may be improved at the expense of others. Such a region is called the Pareto optima (PO) set. A parameterlzation of this set simplifies the attainment of the existing tradeoff. Working within the Pareto set guaranties optimum tradeoff. We present two examples for linear time invariant systems. These examples help illustrate different issues involved in this matter.
Creative Commons Attribution BY 4.0 license
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Systems Research Institute of the Polish Academy of Sciences
Library of Systems Research Institute PAS
Oct 15, 2021
Jul 15, 2021
44
https://rcin.org.pl./publication/234138