MATLAB Simulink modeling and simulation of LVI-based primal–dual neural network for solving linear and quadratic programs

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 li...

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Published inNeurocomputing (Amsterdam) Vol. 72; no. 7; pp. 1679 - 1687
Main Authors Zhang, Yunong, Ma, Weimu, Li, Xiao-Dong, Tan, Hong-Zhou, Chen, Ke
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2009
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Online AccessGet full text
ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2008.07.008

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Abstract 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.
AbstractList 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.
Author Tan, Hong-Zhou
Li, Xiao-Dong
Ma, Weimu
Chen, Ke
Zhang, Yunong
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Keywords Quadratic programs
Circuit implementation
Neural networks
Linear programs
MATLAB Simulink modeling and simulation
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  publication-title: Trans. Am. Inst. Electrical Eng.
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Snippet In view of parallel-processing nature and circuit-implementation convenience, recurrent neural networks are often employed to solve optimization problems....
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StartPage 1679
SubjectTerms Circuit implementation
Linear programs
MATLAB Simulink modeling and simulation
Neural networks
Quadratic programs
Title MATLAB Simulink modeling and simulation of LVI-based primal–dual neural network for solving linear and quadratic programs
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