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Abstract

One of state-of-the-art recurrent neural networks (RNNs) is dual neural network (DNN). It can solve QP in real time. The dual neural network is of simple piecewise-linear dynamics and has global (exponential) convergence to optimal solutions. In this chapter, we first introduce the QP problem formulation and its online solution based on RNN. Some related concepts and definitions are also given. Second, we present the DNN and its design method. In addition to the general design method, for nondiagonal, nonanalytical, and/or time-varying cases, a matrix-inverse neural network could be combined into such a design procedure of the DNN for online computation of its matrix-inverse related term. Third, we show the analysis results of the DNN. Finally, we present a numerical simulation and illustrative example of using the DNN to solve static QP problems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yunong Zhang
    • 1
  • Zhijun Zhang
    • 1
  1. 1.Sun Yat-sen UniversityGuangzhouPeople’s Republic of China

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