基于混沌神经网络模型的预测控制器的设计及应用

根据具有混沌特性非线性、大时滞系统的时间序列重构相空间，计算相空间饱和嵌入维数、并以此为指导，建立混沌神经网络，即便在网络输入不完整或发生变异的情况下，该模型仍能对系统作高精度的短期预测；在此基础上，又设计了模糊神经网络预测控制器，实现了对非线性、大时滞系统高精度的自适应控制。将该控制器应用到单元机组负荷控制系统中，仿真表明了该控制有效性、快速性和鲁棒性。

Design of Fuzzy Neural Network Controller Based on Chaos Neural Network Forecast Model and Application

In order to realize adaptive control of nonlinear big-lagged system, First, a chaotic attractors space is reconstructed, space embed dimension is calculated by the nonlinear big-lagged system chaos time series in this paper. By above all, a chaos neural network model is constructed, which can make high precision short-term forecast for the system even by imperfect and variation inputs. On the basis of this, a fuzzy-neural forecast controller is designed and robust adaptive control to the nonlinear big-lagged chaos system is realized. Last, the controller is applied to units load system, the validity, the high-speed and the robustness are proved by simulate results.