A SUPPORT VECTOR REGRESSION METHOD FOR REDUCING THE HIGH-ORDER SYSTEMS TO FIRST-ORDER PLUS TIME-DELAY FORMS

In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced. For this purpose Support Vector Machines, which became a popular learning algorithm, is employed. Three parameters of the first-order plus time-delay forms are estimated by three parallel support vector regression machines. Satisfactory performance is obtained at the simulations.

Keywords:

Support vector regression machines, high-order systems, first-order plus time-delay systems modeling,

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