Evolutionary model selection for identification of nonlinear parametric systems

Jinyao Yan, J. Deller, Meng Yao, E. Goodman
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引用次数: 3

Abstract

At ChinaSIP 2013, Yan et al. presented a new method for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. This paper reports recent advances in the theoretical foundation of the methods, then focuses on the operation and performance of the approach, particularly the evolutionary model determination. This work opens the door to the use of a broadly generalized class of models with applicability to many contemporary signal processing problems.
非线性参数系统辨识的进化模型选择
在ChinaSIP 2013上,Yan等人提出了一种识别系统模型的新方法,该模型在参数结构上是线性的,但在信号操作中是任意非线性的。该策略将传统的系统识别方法与信号处理中不常用的三种建模策略相结合:线性时不变参数模型、基于集合的参数识别和模型结构的进化选择。本文介绍了该方法的理论基础的最新进展,然后重点介绍了该方法的操作和性能,特别是进化模型的确定。这项工作打开了大门,使用一个广泛的广义类模型,适用于许多当代信号处理问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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