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Zhang neural network solving for time-varying full-rank matrix Moore–Penrose inverse

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Abstract

Zhang neural networks (ZNN), a special kind of recurrent neural networks (RNN) with implicit dynamics, have recently been introduced to generalize to the solution of online time-varying problems. In comparison with conventional gradient-based neural networks, such RNN models are elegantly designed by defining matrix-valued indefinite error functions. In this paper, we generalize, investigate and analyze ZNN models for online time-varying full-rank matrix Moore–Penrose inversion. The computer-simulation results and application to inverse kinematic control of redundant robot arms demonstrate the feasibility and effectiveness of ZNN models for online time-varying full-rank matrix Moore–Penrose inversion.

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Authors and Affiliations

  1. School of Information Science & Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Yunong Zhang, Yiwen Yang, Ning Tan & Binghuang Cai

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  1. Yunong Zhang
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  2. Yiwen Yang
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  3. Ning Tan
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  4. Binghuang Cai
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Correspondence to Yunong Zhang.

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Zhang, Y., Yang, Y., Tan, N. et al. Zhang neural network solving for time-varying full-rank matrix Moore–Penrose inverse. Computing 92, 97–121 (2011). https://doi.org/10.1007/s00607-010-0133-9

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  • DOI: https://doi.org/10.1007/s00607-010-0133-9

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