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- Youshen Xia
- Neural Computation
- 2004

- Youshen Xia, Jun Wang
- IEEE Trans. Neural Networks
- 1998

In this paper, we present a general methodology for designing optimization neural networks. We prove that the neural networks constructed by using the proposed method are guaranteed to be globally convergent to solutions of problems with bounded or unbounded solution sets, in contrast with the gradient methods whose convergence is not guaranteed. We showâ€¦ (More)

- Youshen Xia, Jun Wang
- IEEE Transactions on Neural Networks
- 2004

Recently, a projection neural network for solving monotone variational inequalities and constrained optimization problems was developed. In this paper, we propose a general projection neural network for solving a wider class of variational inequalities and related optimization problems. In addition to its simple structure and low complexity, the proposedâ€¦ (More)

- Youshen Xia, Jun Wang
- IJCNN
- 2000

Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network for solving linear projection equations in real time. The proposed neural network has two layers and is amenable to parallel implementation with simple hardware. In the theoreticalâ€¦ (More)

This paper presents two types of recurrent neural networks, continuous-time and discrete-time ones, for solving linear inequality and equality systems. In addition to the basic continuous-time and discrete-time neural-network models, two improved discrete-time neural networks with faster convergence rate are proposed by use of scaling techniques. Theâ€¦ (More)

- Youshen Xia
- IEEE Trans. Neural Networks
- 1996

Presents a new neural network which improves existing neural networks for solving general linear programming problems. The network, without setting parameter, uses only simple hardware in which no analog multipliers are required, and is proved to be completely stable to the exact solutions. Moreover, using this network the author can solve linearâ€¦ (More)

- Youshen Xia
- IEEE Trans. Neural Networks
- 1996

A new neural network for solving linear and quadratic programming problems is presented and is shown to be globally convergent. The new neural network improves existing neural networks for solving these problems: it avoids the parameter turning problem, it is capable of achieving the exact solutions, and it uses only simple hardware in which no analogâ€¦ (More)

- Youshen Xia, Jun Wang
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2001

The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dualâ€¦ (More)

- Youshen Xia, Gang Feng, Jun Wang
- IEEE Transactions on Neural Networks
- 2008

This paper presents a novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semidefinite, it is shown that the proposed neural network is stable at a Karush-Kuhn-Tucker point in the sense of Lyapunov and its outputâ€¦ (More)

- Yunong Zhang, Jun Wang, Youshen Xia
- IEEE Trans. Neural Networks
- 2003

In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorporated into the problem formulation of redundancy resolution.â€¦ (More)