基于神经网络的工业大系统辨识及稳态递阶优化方法
Identification and Steady-State Hierarchical Optimization Method for Large-Scale Industrial Systems with Neural Network
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摘要: 为了对工业大系统进行稳态递阶优化,必须首先获得系统的稳态模型.从神经网络的分 析人手,给出了工业大系统稳态模型的动态辨识方法及基于神经网络模型的推导方法.为了 提高算法的收敛速度,引入Lagrange函数解决大系统优化问题中的各种约束,并用Hopfield 网络实现了大系统稳态递阶优化的网络算法,最后给出了某一大系统辨识及优化的仿真结果.
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关键词:
- 稳态递阶优化 /
- Hopfield神经网络 /
- 前向神经网络 /
- 工业大系统
Abstract: In order to do steady-state hierarchical optimization for large-scale industrial systems, the steady-state model of the system must be obtained. By means of neural network, this paper presents a dynamic identification method for steady-state models of large-scale industrial systems with neural network, and proposes a way for modelling. For improving convergence, this paper firstly introduces Lagrange function to solve constraint problem in large-scale system optimization, secondly constructs the hierarchical optimization networks for large-scale industrial systems with Hopfield network. -
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