摘要
本文针对现有辨识算法所存在的缺陷 ,作了如下改进工作 :提出了一种新的模型选项准则 ([ERR]准则 ) ,克服了原 [err]准则易导致冗余项被错误选入的缺陷 ,保证了模型结构的正确辨识 ;对原算法做了较大改进 ,克服了原算法使用时遭遇的存储困难问题 ,极大程度地改善了辨识算法的数值稳定性 .理论分析及仿真结果均证明了改进算法的优越性及有效性 .
The NARMAX model identification for nonlinear dynamical systems much depends on the correction of the system structure and the precision of the parameter estimate. The algorithms existed at present for determining the model’s structure are all based on the \ selecting rule, by which the spare terms can easily be firstly introduced with incorrectness, resulting the true system structure can never be obtained. Furthermore, the numerical stability of these algorithms is also unsatisfied, often resulting an unacceptable precision for the parameter estimation. In this paper, the following modification work has been done: a new selecting rule (\ rule) has been proposed to judge the importance of each term compared to all the other terms, which has been selected by the forward process, deleting spare terms and obtaining the true optimal structure for the nonlinear system. Meanwhile, the modified identification algorithm overcomes the storage difficulty resulted by the number explosion of the terms of the assumed NARMAX whole model while applying the traditional MGS algorithm, avoids the backward substitution calculating process, improving to a great extent the numerical stability of the algorithm, and thus obtaining a high precision for the parameter estimation. Theoretical analysis and the simulation results indicate its superiority and effectiveness.
出处
《信息与控制》
CSCD
北大核心
2000年第2期102-110,共9页
Information and Control
