Abstract
A special class of recurrent neural network (RNN), i.e., Zhang neural network (ZNN), has been proposed for a decade for solving online various time-varying problems. In this paper, we generalize and investigate a continuous-time ZNN model for online solution of the time-varying convex quadratic programming (QP) subject to a time-varying linear equality constraint. For the purpose of possible hardware (e.g., digital-circuit or digital-computer) realization, discrete-time ZNN models and numerical algorithms (i.e., discrete-time ZNN algorithms, in short) are proposed and developed by using Euler difference rules. Computer-simulation and numerical results demonstrate the efficacy and accuracy of the presented continuous-time ZNN model and the proposed discrete-time ZNN algorithms for solving online time-varying QP problems.
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References
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© 2012 Springer-Verlag Berlin Heidelberg
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Ke, Z., Yang, Y., Zhang, Y. (2012). Discrete-Time ZNN Algorithms for Time-Varying Quadratic Programming Subject to Time-Varying Equality Constraint. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_6
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DOI: https://doi.org/10.1007/978-3-642-31346-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31345-5
Online ISBN: 978-3-642-31346-2
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