摘要
目的为解决一类同时含有等式约束和不等式约束的混杂约束的非线性规划问题.方法利用神经网络具有内在大规模并行运算和快速收敛特性理论,提出了一种非线性优化神经网络解决一类混杂约束的非线性规划问题.结果该模型既克服了采用罚函数方法的神经网络求解优化问题的缺陷,同时与引入松弛变量的优化神经网络相比,具有电路实现简单、计算量小和收敛速度快等特点.此外,利用能量函数对神经网络的稳定性和收敛性进行了分析,进而保证所提出的神经网络具有全局稳定性.结论通过两个数值仿真例子验证了所提出的优化网络的有效性.
This paper presents a new optimized neural network for nonlinear convex programming problems with both quality and inequality constraints. The proposed neural network avoids the deficiency of the penalty function approach. Meanwhile, the present network needs less neuron than that of slack variables approach, which leads to simple circuit implementation, reduced computation burden and fast convergence. On the basis of energy function, the stability and convergence of the proposed neural network are analyzed, which guarantee the global stability of the equilibrium point of neural network. Two numerical examples are used to demonstrate the effectiveness of the proposed neural network.
出处
《沈阳建筑大学学报（自然科学版）》
CAS
北大核心
2009年第1期196-200,共5页
Journal of Shenyang Jianzhu University：Natural Science
基金
国家自然科学基金项目(60774093)
东北大学博士后科研基金项目(20080314)
关键词
非线性规划
混杂约束
优化计算
神经网络
nonlinear programming
hybrid constraints
optimal computation
neural network
