非线性状态依赖参数模型的非参数模型结构辨识与参数效率

P. Young
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引用次数: 7

摘要

尽管神经模糊模型为非线性系统的基于数据的建模提供了一种非常有用的通用方法,但它们正常的“黑箱”性质往往阻碍了它们在许多自然科学中的使用,在这些自然科学中,通常需要用微分方程或等效差分方程表示,并且模型系统的内部功能和物理意义是建模练习的一个重要方面。此外,模型内部结构的识别可以大大简化模型并避免过度参数化,这对模型参数估计的统计效率有重要影响。本文介绍了一种基于递归固定区间平滑的非参数模型结构识别方法,并说明了它如何在随机动态系统的最终参数化建模中发挥优势
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Parametric Model Structure Identification and Parametric Efficiency in Nonlinear State Dependent Parameter Models
Although neuro-fuzzy models provide a very useful general approach to the data-based modelling of nonlinear systems, their normal 'black box' nature is often a deterrent to their use in many of the natural sciences, where representation in terms of differential equations, or equivalent difference equations, is normally required and where the internal functioning and physical meaning of the model system is an important aspect of the modelling exercise. Moreover, identification of the model's internal structure can lead to considerable simplification of the model and the avoidance of over-parameterization, with important consequences as regards the statistical efficiency of the model parameter estimates. This paper introduces a non-parametric approach to model structure identification, based on recursive fixed interval smoothing, and shows how it can prove advantageous in the final parametric modelling of stochastic dynamic systems
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