一种基于动态规划策略的离散动态大系统递阶优化神经网络
A Dynamic Programming Neural Network for Large-Scale Optimization Problems
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摘要: 给出了一种具有集成化特征的、快速求解大规模系统动态规划问题的神经网络模型 (LDPNN),该神经网络将大系统的各子系统的动态方程约束嵌入局部优化子网络,使得整个 网络的结构简洁、紧凑,便于硬件实现,该神经网络计算模型克服了数值方法迭代计算的缺 陷,求解效率高,适宜于大规模动态系统实时优化应用.Abstract: To overcome the difficulties existing in the conventional numerical methods, a novel neural network (LDPNN) for solving large-scale dynamic programming problems is developed in the paper. The dimension of the neural network is reduced greatly by imbedding the dynamic equations of the subsystems into the corresponding local optimization networks. Moreover, the coordination and local optimization are carried out simultaneously. Therefore, the LDPNN not only has concise and compact structure ,but also has high efficiency for problem-solving. An demonstrative example shows that the LDPNN is suitable for real-time optimization of large-scale systems.
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