摘要
为有限资源均衡问题提供一个神经网络解决方法.首先提出增广置位矩阵,描述了资源均衡的神经网络表示,使得神经元的输出和问题的解彼此对应起来;然后在时间和资源约束下利用多种技巧构造网络的能量函数,使其能量最小值对应于资源最均衡的状态;并且提出基于“权值状态发生器”的离散Hopfield与模拟退火算法(DHNN-SA)融合的镶嵌式混合结构,从本质上提高了网络的优化质量;最后设计了资源优化神经网络的模拟程序.
This paper provides a Neural Network solution for resource leveling problem. In order to give the neural network description of resource leveling problem and make the output eorrespondent with neurons, the new concept of Augmented permute matrix is proposed. Some novel technologies arc using when setting up the energy function under time and resources constrains. An Embedded Hybrid Model combining Discrete-time Hopfield model and SA （DHNNSA） is put forward to improve the optimization in essence in which Hopfield servers as State Generator for the SA. At last, the simulating program built on DHNN-SA is created. The results of the comparing with professional project management software show that the energy function and hybrid model given in this paper is highly efficient in solving resource leveling problem to some extent.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2006年第3期83-87,共5页
Systems Engineering-Theory & Practice
