非线性量化CMAC研究与应用

Research and application of nonlinear quantization CMAC

提出了基于非线性量化小脑模型神经网络(CMAC)算法,对CMAC的概念映射进行了自适应设计,提高CMAC的计算速度和精度以满足复杂动态环境下的非线性实时控制的需要。结合溶出预脱硅系统工艺优化的需求,提出了基于非线性量化CMAC的溶出预脱硅系统时间序列预测模型,用于准确实时地预测循环母液加入量,在此基础上进行循环母液投放措施优化。工业实验说明了该模型在对化工软计算的预测精度和快速性上具有明显的优越性,该模型已应用于某氧化铝厂工艺优化系统中动态调节循环母液投放量,节省了生产成本,取得了明显的经济效益。

The Cerebella Model Articulation Controller（CMAC） based on nonlinear quantization to adjust the concept mapping is presented.The CMAC based on nonlinear quantization improves the speed and accuracy of calculation to meet the complex and dynamic demand in the nonlinear environment for real-time control.Considering the demand of the process optimization of digesting pre-silicon systems,a proportion mixture time series prediction model of digesting recycled liquor by CMAC based on nonlinear quantization is presented to forecast the quantity of recycled liquor accurately and fast.The quantity of recycled liquor is optimized in this application.Industry test shows that the accuracy and rapid of the time series prediction model has obvious advantages than other models.And the model has been applied to certain alumina plant to optimize dynamically the recycled liquor quantity to save the cost of production and achieve obvious economic benefits.