CAJ | 학술논문

根据人工神经网络的基本优化机理，提出了一种基于Lagrange函数的适合于求解二次规划问题的神经网络模型，研究了该神经网络的稳定性和收敛性，探讨了提高网络优化计算效率的神经优化策略，仿真结果证明了该神经网络能有效地求解二次规划问题.
근거인공신경망락적기본우화궤리，제출료일충기우Lagrange함수적괄합우구해이차규화문제적신경망락모형，연구료해신경망락적은정성화수렴성，탐토료제고망락우화계산효솔적신경우화책략，방진결과증명료해신경망락능유효지구해이차규화문제.
According to the basic optimization principle of artificial neural networks, a Lagrange function based neural network model approach appropriate for quadratic programming problems is given. Stability and convergence of the neural network are investigated. Neural optimization strategy to improve the neural computing efficiency is studied. Simulation demonstrates that these neural networks can effectively solve quadratic programming problems.The new network is easy to construct with circuits, capable of achieving the exact solution.