1. Zhang neural network versus gradient-based neural network for time-varying linear matrix equation solving
- Author
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Guo, Dongsheng, Yi, Chenfu, and Zhang, Yunong
- Subjects
- *
ARTIFICIAL neural networks , *EQUATIONS , *COMPUTER simulation , *EXPONENTIAL functions , *STOCHASTIC convergence , *PERFORMANCE evaluation , *COMPARATIVE studies - Abstract
Abstract: A type of recurrent neural networks called Zhang neural network (ZNN) is presented and investigated to provide an online solution to the time-varying linear matrix equation, by using a novel design method. In contrast to the gradient-based neural network (GNN), the novel design of ZNN is based on a matrix-valued indefinite error function, instead of a scalar-valued norm-based energy function. Therefore, a ZNN model depicted in implicit dynamics can globally and exponentially converge to the time-varying theoretical solution of the given linear matrix equation. Computer simulation results further demonstrate the superior performance of the ZNN model in solving the time-varying linear matrix equation compared with the conventional GNN model. [Copyright &y& Elsevier]
- Published
- 2011
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