Abstract
A lower order discrete-time recurrent neural network is presented in this paper for solving higher quadratic programming. It bases on the orthogonal decomposition method and solves high order quadratic programs, especially for the case that the number of decision variables is close to the number of its constraints. The proposed recurrent neural network is globally exponential stability and converges to the optimal solutions of the higher quadratic programming. The condition for the neural network to globally converge to the optimal solution of the quadratic program is given. An illustrative example and the simulation results are presented to illustrate its performance.
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Liao, W., Wang, J., Wang, J. (2010). A Lower Order Discrete-Time Recurrent Neural Network for Solving High Order Quadratic Problems with Equality Constraints. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_25
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DOI: https://doi.org/10.1007/978-3-642-13278-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
Online ISBN: 978-3-642-13278-0
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