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Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment

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2 Author(s)
Yunong Zhang ; Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China ; Jun Wang

Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements

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Neural Networks, IEEE Transactions on  (Volume:13 ,  Issue: 3 )