基于超闭球小脑模型的系统建模及其校正  

Hyperball Cerebellar Model Articulation Controller (CMAC) Based System Modeling and Its Modifying Strategies

作　　者：段培永[1] 张玫[1] 段晨旭[1] 邵惠鹤[2] 

机构地区：[1]山东建筑工程学院信息与电气工程系,济南250014 [2]上海交通大学自动化系,上海200030

出　　处：《上海交通大学学报》2005年第8期1336-1340,共5页Journal of Shanghai Jiaotong University

基　　金：山东省自然科学基金资助项目(Q2002G01)

摘　　要：为了克服神经网络建模在工程应用中的不足,利用超闭球小脑模型(HCMAC)神经网络所具有的结构简单、学习收敛速度快、泛化能力强等优势,提出了基于HCMAC的非线性动态系统建模原理.分析了建模误差产生的原因,给出了基于误差校正率的神经网络模型多步在线校正策略,采用通过实时扩展模型学习样本空间和基于模型误差可信度的模型参数修正方法训练模型,以跟踪实际动态过程.仿真实验证明上述方法可有效地减小由于样本精度不高和在模型输入空间中的分布不均匀所带来的初始模型误差,同时可实时适应非线性动态过程工况的变化.Hyperball cerebellar model articulation controller （HCMAC） has advantages such as simple structure, fast learning convergence and powerful generalization capability. In order to overcome the shortcomings of the conventional neural networks based modeling methods, the HCMAC based modeling methods of nonlinear dynamic systems were presented. The modifying error ratio based multiple-stepped on-line modifying strategy was given according to the qualitatively analyzed causes leading to modeling errors. In order to exactly gain the dynamic performance changed with a variant operation point of a certain industry process, the model expands its learning sample data space pertinent to the variation to gain more information near the operation point. In addition, part of each error between real output of the plant and the estimated by network model is applied to iteratively train the model on-line. The simulation results demonstrate that the methods are capable of decreasing the initial model errors resulting from the insufficient or/and inaccurate learning sample data, and the model can be easily and on-line adjusted to follow a practical varied operation point of the nonlinear dynamic process.

关 键 词：小脑模型 非线性动态系统 建模 校正策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]