Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 48, Issue 11, Pages 3135-3148Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2760883
Keywords
Computer simulations; convergence and robustness; recurrent neural networks; time-varying equation solving
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Funding
- National Natural Science Foundation [61603142]
- Science and Technology Program of Guangzhou [201707010225]
- Fundamental Research Funds for Central Universities [2017MS049]
- Guangdong Natural Science Funds for Distinguished Young Scholar [2017A030306009]
- Scientific Research Starting Foundation of South China University of Technology
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Solving Sylvester equation is a common algebraic problem in mathematics and control theory. Different from the traditional fixed-parameter recurrent neural networks, such as gradient-based recurrent neural networks or Zhang neural networks, a novel varying-parameter recurrent neural network, [called varying-parameter convergent-differential neural network (VP-CDNN)] is proposed in this paper for obtaining the online solution to the time-varying Sylvester equation. With time passing by, this kind of new varying-parameter neural network can achieve super-exponential performance. Computer simulation comparisons between the fixed-parameter neural networks and the proposed VP-CDNN via using different kinds of activation functions demonstrate that the proposed VP-CDNN has better convergence and robustness properties.
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