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3 results on '"Yi, Chenfu"'

1. An adaptive gradient-descent-based neural networks for the on-line solution of linear time variant equations and its applications.

Author

Cai, Jun and Yi, Chenfu

Subjects
*RECURRENT neural networks, *EQUATIONS
Abstract

It is well-known that, the classical gradient-descent-based neural network (CGNN) model is used widely for the time-invariant problem solving. However, it is an extremely common problem for the time varying cases in the practical engineering applications, while the CGNN effective neural solver for the time-variant problems. For this reason, in this article, an adaptive GNN (AGNN) is presented for the linear time variant matrix equation (LTVME) on-line solving based on Lyapunov theory. Theoretical analysis already verified that the presented AGNN model could achieve the correct state solution effectively. The simulated experiment results further validate that the state solution of the AGNN model could be convergent to the correct solution of the solved time variant problems in theory. [ABSTRACT FROM AUTHOR]

Published
2023
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2. An adaptive gradient-descent-based neural networks for the on-line solution of linear time variant equations and its applications.

Author

Cai, Jun and Yi, Chenfu

Subjects
*RECURRENT neural networks, *EQUATIONS
Abstract

It is well-known that, the classical gradient-descent-based neural network (CGNN) model is used widely for the time-invariant problem solving. However, it is an extremely common problem for the time varying cases in the practical engineering applications, while the CGNN effective neural solver for the time-variant problems. For this reason, in this article, an adaptive GNN (AGNN) is presented for the linear time variant matrix equation (LTVME) on-line solving based on Lyapunov theory. Theoretical analysis already verified that the presented AGNN model could achieve the correct state solution effectively. The simulated experiment results further validate that the state solution of the AGNN model could be convergent to the correct solution of the solved time variant problems in theory. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Zhang neural network versus gradient-based neural network for time-varying linear matrix equation solving

Author

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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