Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 16, Issue 10, Pages 6359-6369Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2964817
Keywords
Mathematical model; Computational modeling; Acoustics; Convergence; Informatics; Recurrent neural networks; Task analysis; Acoustic source localization; mobile manipulator; recurrent neural network (RNN); time-variant generalized Sylvester equation (TVGSE)
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Funding
- National Natural Science Foundation of China [61703189]
- National Key Research and Development Program of China [2017YFE0118900]
- Natural Science Foundation of Gansu Province, China [18JR3RA264]
- Sichuan Science and Technology Program [19YYJC1656]
- Fundamental Research Funds for the Central Universities [lzujbky-2019-89]
- Innovation and Strength Project in Guangdong Province (Natural Science) [230419065]
- Industry-University-Research Cooperation Education Project of Ministry of Education [201801328005]
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A generalized Sylvester equation is a special formulation containing the Sylvester equation, the Lyapunov equation and the Stein equation, which is often encountered in various fields. However, the time-variant generalized Sylvester equation (TVGSE) is rarely investigated in the existing literature. In this article, we propose a noise-suppressing recurrent neural network (NSRNN) model activated by saturation-allowed functions to solve the TVGSE. For comparison, the existing zeroing neural network (ZNN) models and some improved ZNN models are introduced. Additionally, theoretical analysis on the convergence and robustness of the NSRNN model is given. Furthermore, computer simulations on illustrative examples and applications to robots and acoustic source localization are carried out. Validation results synthesized by the NSRNN model and other ZNN models are provided to illustrate the ability in solving the TVGSE and dealing with noises of the NSRNN model, and the inaction of other ZNN models to noises.
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