Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Xuanjiao Lv"'
Publikováno v:
IEEE Transactions on Industrial Informatics. 16:1591-1601
Dynamic (or say, time-varying) problems have been a hot spot of research recently. As a general form of matrix inverse, dynamic Moore–Penrose inverse solving has received more and more attention owing to its broad applications. The approaches based
Publikováno v:
Information Processing Letters. 147:88-93
This paper proposes an improved Zhang neural network (IZNN) for time-varying linear system of equations solving. Such a neural network is activated by an array of continuous sign-bi-power function. Theoretical analysis is provided to show the desired
Publikováno v:
Neural Processing Letters. 50:1993-2005
Being with parallel-computation nature and convenience of hardware implementation, linear gradient neural networks (LGNN) are widely used to solve large-scale online matrix-involved problems. In this paper, two improved GNN (IGNN) models, which are a
Publikováno v:
Asian Journal of Control. 22:1188-1196
In this paper, two novel neural networks (NNNs), namely NNN‐L and NNN‐R neural models, are proposed to online left and right Moore‐Penrose inversion. As compared to GNN (gradient neural ne...
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030002138
This paper develops a new implicit neural dynamic to real-time find the inverse of time-invariant (or say static, constant) matrix. Such a neural model is proven to have higher convergence rate than other existing neural models, specifically, the gra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb0aca1c4dd8dde5922665d00a4e8ef5
https://doi.org/10.1007/978-3-030-00214-5_28
https://doi.org/10.1007/978-3-030-00214-5_28
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319659770
In this paper, a new recurrent neural network (RNN) model is proposed and investigated for solving real-time linear system of equations. The proposed model has an advantage over the existing RNNs, specifically, the gradient-based neural network, Zhan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::573376efba3d894c71b0c4363e28d8da
https://doi.org/10.1007/978-3-319-65978-7_22
https://doi.org/10.1007/978-3-319-65978-7_22
Publikováno v:
Robotics and Autonomous Systems. 57:645-651
In this paper, a dual neural network, LVI (linear variational inequalities)-based primal-dual neural network and simplified LVI-based primal-dual neural network are presented for online repetitive motion planning (RMP) of redundant robot manipulators
Publikováno v:
Mechatronics. 18:475-485
In this paper, a primal–dual neural network based on linear variational inequalities (LVI) is presented for the online repetitive motion planning of PA10 robot arm, a kinematically redundant manipulator. To do this, a drift-free criterion is exploi
Publikováno v:
IJCNN
This paper presents a simplified primal-dual neural network based on linear variational inequalities (LVI) for online repetitive motion planning of PA10 robot manipulator. To do this, a drift-free criterion is exploited in the form of a quadratic fun
Publikováno v:
2008 International Conference on Information and Automation.
In this paper, a recurrent neural network (termed, dual neural network) is revisited and applied to the online joint angle drift-free redundancy-resolution of a five-link planar robot manipulator. To do this, a drift-free criterion is exploited in th