基于Hopfield网络的不可分稳态大系统递阶优化控制

研究了整体目标函数关于各子系统具有不可加形式的大系统稳态优化控制问题 ,针对利用多目标优化技术把不可分问题转化为可分问题时采用的迭代策略使得计算较慢的问题 ,提出了具有递阶结构的基于 IPM的 Hopfield优化网络 ,并证明了该网络是渐进稳定的 ,其平衡点为原问题的最优点 .仿真表明 ,这是解决不可分稳态大系统优化问题的有效途径.

Hierarchical Optimization Control Based on Hopfield Network for Nonseparable Steady State Large Scale System

Considered is the nonseparable steady state optimization control problem for large-scale systems, where the overall objective function is not of an additive form with respect to subsystems. To deal with the slow compute problem using iterative algorithms in multiobjective optimization, Hopfield hierarchical optimization network based on IPM is presented. Its asymptote stability is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.