Abstract
The paper proposes an alternative control design for the chaotic Lorenz system based on neural networks. The controller is a feedforward neural network trained by a model reference technique. Implementation of the control design requires system states for feedback, while in most of practical applications only the system output is available. To overcome this problem, a nonlinear observer is used to estimate the states of the system. Simulation results have illustrated the feasibility and effectiveness of the proposed observer-based neural network controller.
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Kuntanapreeda, S. (2008). An Observer-Based Neural Network Controller for Chaotic Lorenz System. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_67
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DOI: https://doi.org/10.1007/978-3-540-92137-0_67
Publisher Name: Springer, Berlin, Heidelberg
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