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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4
A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
ASCII Text
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Ruangrit Ekachaiworasin, Suwat Kuntanapreeda, "A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 4, pp. 4325, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.860792,
author = {Ruangrit Ekachaiworasin and Suwat Kuntanapreeda},
title = {A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {4},
year = {2000},
issn = {1098-7576},
pages = {4325},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860792},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems
SN - 1098-7576
SP
EP
A1 - Ruangrit Ekachaiworasin,
A1 - Suwat Kuntanapreeda,
PY - 2000
VL - 4
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Ruangrit Ekachaiworasin, King Mongkut's Institute of Technology North Bangkok
Suwat Kuntanapreeda, King Mongkut's Institute of Technology North Bangkok
A training rule for neural network controllers that guarantees finite-region stability of control systems has recently developed. The training rule requires that the controlled systems must be locally hermitian, controllable, and full state accessible. The controller is a single hidden layer feedforward networks. This present paper extends the training rule by modifying the stability condition to drop out the hermitian requirement. A finite stability region is estimated by evaluating an existing Lyapunov function, which is a by-product of the training rule.
Citation:
Ruangrit Ekachaiworasin, Suwat Kuntanapreeda, "A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems," ijcnn, vol. 4, pp.4325, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000
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