期刊
NEUROCOMPUTING
卷 72, 期 7-9, 页码 1679-1687出版社
ELSEVIER
DOI: 10.1016/j.neucom.2008.07.008
关键词
Neural networks; Circuit implementation; Linear programs; Quadratic programs; MATLAB Simulink modeling and simulation
资金
- National Science Foundation of China [60775050]
- Science and Technology Office of Sun Yat-Sen University
In view of parallel-processing nature and circuit-implementation convenience, recurrent neural networks are often employed to solve optimization problems. Recently, a primal-dual neural network based on linear variational inequalities (LVI) was developed by Zhang et al. for the online solution of linear-programming (LP) and quadratic-programming (QP) problems simultaneously subject to equality, inequality and bound constraints. For the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, we investigate in this paper the MATLAB Simulink modeling and simulative verification of such an LVI-based primal-dual neural network (LVI-PDNN). By using click-and-drag mouse operations in MATLAB Simulink environment, we could quickly model and simulate complicated dynamic systems. Modeling and simulative results substantiate the theoretical analysis and efficacy of the LVI-PDNN for solving online the linear and quadratic programs. (C) 2008 Elsevier B.V. All rights reserved.
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