Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function

Neural Processing Letters

Volumn 37, Issue 2, 2013, Pages 189-205

  Li, Shuai ^a   Chen, Sanfeng ^b   Liu, Bo ^c  

^a Stevens Institute of Technology   (United States)

^b SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY   (China)

^c Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Finite time convergence; Global convergence; Sylvester equation; Zhang neural network

Indexed keywords

ACTIVATION FUNCTIONS; FINITE-TIME CONVERGENCE; GLOBAL CONVER-GENCE; INVERTED PENDULUM SYSTEM; REAL-TIME CALCULATIONS; REALTIME PROCESSING; SYLVESTER EQUATION; ZHANG NEURAL NETWORKS;

COMPUTATIONAL COMPLEXITY; PENDULUMS; RECURRENT NEURAL NETWORKS;

ACTIVATION ANALYSIS;

EID: 84879688364     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-012-9241-1     Document Type: Article

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