A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application

[1]  Shuai Li,et al.  Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks , 2012, Neurocomputing.

[2]  Qingshan Liu,et al.  Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions , 2011, IEEE Transactions on Neural Networks.

[3]  Jun Wang,et al.  Analysis and Design of a $k$ -Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function , 2010, IEEE Transactions on Neural Networks.

[4]  Tetsuya Ogata,et al.  Inter-modality mapping in robot with recurrent neural network , 2010, Pattern Recognit. Lett..

[5]  Changyin Sun,et al.  A novel neural dynamical approach to convex quadratic program and its efficient applications , 2009, Neural Networks.

[6]  Youshen Xia,et al.  A Compact Cooperative Recurrent Neural Network for Computing General Constrained $L_1$ Norm Estimators , 2009, IEEE Transactions on Signal Processing.

[7]  Xiaolin Hu,et al.  A New Recurrent Neural Network for Solving Convex Quadratic Programming Problems With an Application to the $k$-Winners-Take-All Problem , 2009, IEEE Transactions on Neural Networks.

[8]  Xiaolin Hu,et al.  An Improved Dual Neural Network for Solving a Class of Quadratic Programming Problems and Its $k$-Winners-Take-All Application , 2008, IEEE Transactions on Neural Networks.

[9]  Qingshan Liu,et al.  Two k-winners-take-all networks with discontinuous activation functions , 2008, Neural Networks.

[10]  John G. Harris,et al.  Noise-Robust Automatic Speech Recognition Using a Predictive Echo State Network , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  Shubao Liu,et al.  A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application , 2006, IEEE Transactions on Neural Networks.

[12]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[13]  Jun Wang,et al.  A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations , 2004, Neural Networks.

[14]  V. Singh Robust stability of cellular neural networks with delay: linear matrix inequality approach , 2004 .

[15]  Suk-Geun Hwang,et al.  Cauchy's Interlace Theorem for Eigenvalues of Hermitian Matrices , 2004, Am. Math. Mon..

[16]  Jun Wang,et al.  A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits , 2003, IEEE Trans. Neural Networks.

[17]  Peter Stagge,et al.  Recurrent neural networks for time series classification , 2003, Neurocomputing.

[18]  Yunong Zhang,et al.  A dual neural network for convex quadratic programming subject to linear equality and inequality constraints , 2002 .

[19]  Jun Wang,et al.  A dual neural network for kinematic control of redundant robot manipulators , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Dennis S. Bernstein,et al.  Finite-Time Stability of Continuous Autonomous Systems , 2000, SIAM J. Control. Optim..

[21]  Kate Smith-Miles,et al.  Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research , 1999, INFORMS J. Comput..

[22]  W K Chen,et al.  A high-performance neural network for solving linear and quadratic programming problems , 1996, IEEE Trans. Neural Networks.

[23]  Mahesan Niranjan,et al.  The use of recurrent neural networks for classification , 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.

[24]  Shengwei Zhang,et al.  Lagrange programming neural networks , 1992 .

[25]  Edgar Sanchez-Sinencio,et al.  Nonlinear switched capacitor 'neural' networks for optimization problems , 1990 .

[26]  Leon O. Chua,et al.  Neural networks for nonlinear programming , 1988 .

[27]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.