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Rapid dynamical pattern recognition for sampling sequences

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Abstract

In this paper, based on deterministic learning, we propose a method for rapid recognition of dynamical patterns consisting of sampling sequences. First, for the sequences yielded by sampling a periodic or recurrent trajectory (a dynamical pattern) generated from a nonlinear dynamical system, a sampled-data deterministic learning algorithm is employed for modeling/identification of inherent system dynamics. Second, a definition is formulated to characterize similarities between sampling sequences (dynamical patterns) based on differences in the system dynamics. Third, by constructing a set of discrete-time dynamical estimators based on the learned knowledge, similarities between the test and training patterns are measured by using the average L1 norms of synchronization errors, and general conditions for accurate and rapid recognition of dynamical patterns are given in a sampled-data framework. Finally, numerical examples are discussed to illustrate the effectiveness of the proposed method. We demonstrate that not only a test pattern can be rapidly recognized corresponding to a similar training pattern, but also the proposed recognition conditions can be verified step by step based on historical sampling data. This makes a distinction compared with the previous work on rapid dynamical pattern recognition for continuous-time nonlinear systems, in which the recognition conditions are difficult to be verified by using continuous-time signals.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 61890922) and in part by National Major Scientific Instruments Development Project (Grant No. 61527811).

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Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Weiming Wu

  2. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Qian Wang & Cong Wang

  3. Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Chengzhi Yuan

Authors
  1. Weiming Wu
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  2. Qian Wang
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  3. Chengzhi Yuan
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  4. Cong Wang
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Correspondence to Cong Wang.

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Wu, W., Wang, Q., Yuan, C. et al. Rapid dynamical pattern recognition for sampling sequences. Sci. China Inf. Sci. 64, 132201 (2021). https://doi.org/10.1007/s11432-019-2878-y

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  • DOI: https://doi.org/10.1007/s11432-019-2878-y

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