基于改进的灰色预测的模糊神经网络控制

采用等维新信息和提高原始数据列光滑度的方法对灰色预测模型进行改进,具有预测更准确的效果,结合采用模糊神经网络控制的精确稳定性特点,提出一种改进的灰色预测的孵化模糊神经网络控制算法,将其应用在具有大滞后、强干扰的孵化过程中。仿真和实际结果表明,提出的控制策略能够有效克服时滞过程的超调问题,具有较强的鲁棒性和自适应性。

Abstract：

Grey Predictive model was improved by using ＂moving window＂ and increasing the smoothness of original data,which can predict accurately,by combining grey predictive model with fuzzy neural network control algorithm which is accurate and stable.A fuzzy neural network control algorithm based on improved grey predictive model was proposed to be applied in incubation process which is lag largely and strongly disturbed.Simulation and running results show that the proposed control strategy can effectively overcome the overshoot caused by delay and has better flexibility and robustness.

Fuzzy Neural Network Control Based on Improved Gray Prediction

Grey Predictive model was improved by using ＂moving window＂ and increasing the smoothness of original data,which can predict accurately,by combining grey predictive model with fuzzy neural network control algorithm which is accurate and stable.A fuzzy neural network control algorithm based on improved grey predictive model was proposed to be applied in incubation process which is lag largely and strongly disturbed.Simulation and running results show that the proposed control strategy can effectively overcome the overshoot caused by delay and has better flexibility and robustness.