Identification and Steady-State Hierarchical Optimization Method for Large-Scale Industrial Systems with Neural Network
基于神经网络的工业大系统辨识及稳态递阶优化方法

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.