This paper presents a one-layer neural network model for solving l_1-norm problems with constraints. Compared with some existing neural network models,the proposed model needs fewer neurons and has a simpler structure. The stability and convergence of the proposed model are proved by introducing a Lyapunov function. Some simulation examples are used to illustrate its validity and transient behaviors.
【基金】
国家自然科学基金资助项目(61273311,61603235)
【更新日期】
2019-01-04
【分类号】
TP183
【正文快照】
0引言考虑min‖Bx-b‖1s. t. Dx∈Θ,x∈X{。(1)其中x=(x1,x2,…,xm)T∈Rm为决策变量,B∈Rn×m,D∈Rp×m和b∈Rn为给定向量和矩阵,Θ={θ∈Rp|l≤θ≤h}和X={x∈Rm|珓l≤x≤h珘}为非空盒子集,l,h∈Rp(l≤h),珓l,h珘∈Rn(珓l≤h珘),l和珓l的某些分量可能为-∞,而h和h珘的某些分?
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