Abstract
This paper presents a feedback recurrent neural network for solving the quadratic programming with quadratic equality constraint (QPQEC) problems based on project theory and energy function. In the theoretical aspect, we prove that the proposed neural network has one unique continuous solution trajectory and the equilibrium point of neural network is stable and convergent when the initial point is given. Employing the idea of successive approximation and convergence theorem from [6], the optimal solution of QPQEC problem can be obtained. The simulation result also shows that the proposed feedback recurrent neural network is feasible and efficient.
This work was jointly supported by the National Natural Science Foundation of China under Grant 60373067, the Natural Science Foundation of Jiangsu Province, China under Grants BK2003053 and BK2004021, and the Foundation of Southern Yangtze University.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yang, Y., Cao, J., Zhu, D. (2005). A Neural Network Methodology of Quadratic Optimization with Quadratic Equality Constraints. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_113
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DOI: https://doi.org/10.1007/11427391_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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