Some results on nonlinear dynamic rational model identification and validation
The Non-linear AutoRegressive Moving Average with eXogenous input (NARMAX) model set has been extensively studied in theory and gradually adopted in applications.There are two main steams of sub-model sets,polynomial and rational.The rational model represents an extension of the polynomial model set,and is defined as a ratio of two polynomial expressions.The introduction of the denominator polynomial terms makes the rational NARMAX model to be non-linear in both the parameters and the regression terms.The justification for using the rational model is that it provides a very concise and parsimonious representation for complex non-linear systems and has excellent interpolatory and extrapolatory properties.However model identification and controller design are much more challenging compared to the polynomial models.This has been a new and fascinating research trend in the area of mathematical modelling and applications,but still within a limited research community.This paper brings several representative algorithms together,developed by the author and his colleagues,to form an easily referenced archive for promotion of awareness,tutorial,applications,and even further research expansion.
Rational models least squares computations neural networks model validation.
Quan Min Zhu
Faculty of Computing,Engineering and Mathematical Sciences (CEMS),University of the West of England (UWE),Frenchay Campus,Coldharbour Lane,Bristol,BS16 1QY,UK
国际会议
International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)
上海
英文
2008-06-29（万方平台首次上网日期，不代表论文的发表时间）