contributor author | Liang Jin | |
contributor author | Peter N. Nikiforuk | |
contributor author | Madan M. Gupta | |
date accessioned | 2017-05-08T23:43:41Z | |
date available | 2017-05-08T23:43:41Z | |
date copyright | December, 1994 | |
date issued | 1994 | |
identifier issn | 0022-0434 | |
identifier other | JDSMAA-26211#567_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/113293 | |
description abstract | A scheme of dynamic recurrent neural networks (DRNNs) is discussed in this paper, which provides the potential for the learning and control of a general class of unknown discrete-time nonlinear systems which are treated as “black boxes” with multi-inputs and multi-outputs (MIMO). A model of the DRNNs is described by a set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obtain the inverse dynamics. The ability of a DRNN structure to model arbitrary dynamic nonlinear systems is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation (DBP) learning algorithm. An equivalent control concept is introduced to develop a model based learning control architecture with simultaneous on-line identification and control for unknown nonlinear plants. The potentials of the proposed methods are demonstrated by simulation results. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems | |
type | Journal Paper | |
journal volume | 116 | |
journal issue | 4 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.2899254 | |
journal fristpage | 567 | |
journal lastpage | 576 | |
identifier eissn | 1528-9028 | |
keywords | Nonlinear systems | |
keywords | Artificial neural networks | |
keywords | Dynamics (Mechanics) | |
keywords | Algorithms | |
keywords | Equations | |
keywords | Industrial plants AND Simulation results | |
tree | Journal of Dynamic Systems, Measurement, and Control:;1994:;volume( 116 ):;issue: 004 | |
contenttype | Fulltext | |