A.S. Aliyeva
Identification of multiconnected dynamic objects with uncertainty based on neural technology and reference converters
The proposed method of identification in the presented article allows evaluating with sufficient accuracy the “black box” model of multiconnected non-stationary dynamic objects with uncertainty in any short time. One of the key advantages of the proposed method is that it allows determining an adaptable "black box" model and creating an adaptive control system in the case of non-measurable derivatives of the m-th order state variables of a multiconnected object.
Keywords: Reference converter-filters, Neural network, Multiconnected dynamic object with uncertainty, “Black box” model
Identification of multiconnected dynamic objects with uncertainty based on neural technology and reference converters
The proposed method of identification in the presented article allows evaluating with sufficient accuracy the “black box” model of multiconnected non-stationary dynamic objects with uncertainty in any short time. One of the key advantages of the proposed method is that it allows determining an adaptable "black box" model and creating an adaptive control system in the case of non-measurable derivatives of the m-th order state variables of a multiconnected object.
Keywords: Reference converter-filters, Neural network, Multiconnected dynamic object with uncertainty, “Black box” model