Neurocontrol of nonlinear dynamic systems subject to unmeasured disturbance inputs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1327, Issue , 1997, Pages 855-860

  Habtom, Ressom ^a   Litz, Lothar ^a  

^a University of Koblenz Landau   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; EXTENDED KALMAN FILTERS; NONLINEAR DYNAMICAL SYSTEMS;

CONTROL SIGNAL; MEASURABLE INPUTS; MODELING AND CONTROL; NETWORK-BASED APPROACH; NEURAL MODELING; NEURO CONTROL; NEURO CONTROLLERS; RECURRENT NEURAL NETWORK MODEL;

RECURRENT NEURAL NETWORKS;

EID: 84956629896     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0020261     Document Type: Conference Paper

References (7)

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2. G. V. Puskorius and L. A. Feldkamp. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks, IEEE Trans, on Neural Networks, vol. 5, pp. 279-297, 1994.
3. R. Habtom and L. Litz. Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter, in Proc. of International Conference on Neural Networks, Houston 1997, vol. 4, pp. 2067-2071.
4. B. Fernandez, A. G. Parios, and W. K. Tsai, Nonlinear dynamic system identification using artificial neural networks (ANNs), in Proc. of International Joint Conference on Neural Networks, San Diego 1990, vol. 2, pp. 131-141.
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6. R. J. Williams and D. Zipser. A learning algorithm for continually running folly recurrent neural networks, Neural Computation, vol. 1, pp. 270-280,1989.
7. R. Habtom and L. Litz. Neural network-based control of nonlinear dynamic systems with unmeasured inputs, in Proc. of Twelfth International Conference on Systems Engineering, Coventry, UK, 1997.