Journal
IEEE ACCESS
Volume 6, Issue -, Pages 77940-77952Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2884497
Keywords
Neural network models; matrix equations; time-varying systems; convergence analysis
Categories
Funding
- National Key R&D Program of China [2017YFB1002505]
- National Natural Science Foundation of China [61603142, 61633010]
- Guangdong Foundation for Distinguished Young Scholars [2017A030306009]
- Guangdong Youth Talent Support Program of Scientific and Technological Innovation [2017TQ04X475]
- Science and Technology Program of Guangzhou [201707010225]
- Fundamental Research Funds for Central Universities [2017MS049]
- Scientific Research Starting Foundation of South China University of Technology, National Key Basic Research Program of China (973 Program) [2015CB351703]
- Natural Science Foundation of Guangdong Province [2014A030312005]
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In mathematics and engineering fields, solving online time-varying matrix equation P(t)X(t)Q(t) = W(t) problem is fundamental and vital. A novel varying-gain recurrent neural network (VG-RNN) is proposed to obtain the online solution of such time-varying matrix equation problem. Distinguished from the conventional gradient-based neural network (termed as GNN), zeroing neural network (termed as ZNN), and finite-time zeroing neural network (termed as FTZNN), the design parameter of VG-RNN is changing with time t goes. Theoretical analysis proves that VG-RNN achieves super-exponential convergent performance when solving the online time-varying matrix equation. Simulation comparisons illustrate that VG-RNN possesses the capability of faster convergence rate than that of GNN, ZNN, and FTZNN. Besides, activated by different activation functions, the convergence performance of VG-RNN can be improved.
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