Stability analysis of RBF network-based state-dependent autoregressive model for nonlinear time series

Min Gan; Hui Peng

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Stability analysis of RBF network-based state-dependent autoregressive model for nonlinear time series

机译：基于RBF网络的状态相关的非线性时间序列自回归模型的稳定性分析

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Varying-coefficient models have attracted great attention in nonlinear time series analysis recently. In this paper, we consider a semi-parametric functional-coefficient autoregressive model, called the radial basis function network-based state-dependent autoregressive (RBF-AR) model. The stability conditions and existing conditions of limit cycle of the RBF-AR model are discussed. An efficient structured parameter estimation method and the modified multi-fold cross-validation criterion are applied to identify the RBF-AR model. Application of the RBF-AR model to the famous Canadian lynx data is presented. The forecasting capability of the RBF-AR model is compared to those of other competing time series models, which shows that the RBF-AR model is as good as or better than other models for the postsample forecasts.

机译：变系数模型最近在非线性时间序列分析中引起了极大的关注。在本文中，我们考虑了半参数函数系数自回归模型，称为基于径向基函数网络的状态相关自回归（RBF-AR）模型。讨论了RBF-AR模型的稳定性条件和极限环的现有条件。一种有效的结构化参数估计方法和改进的多重交叉验证准则被应用于识别RBF-AR模型。介绍了RBF-AR模型在加拿大著名的山猫数据中的应用。将RBF-AR模型的预测能力与其他竞争性时间序列模型的预测能力进行了比较，这表明RBF-AR模型在后采样预测方面与其他模型一样好或更好。

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《Applied Soft Computing》 |2012年第1期|共8页
作者
Min Gan; Hui Peng;
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正文语种 eng
中图分类计算机软件;
关键词
Radial basis function network; State-dependent autoregressive model; Stability analysis; Limit cycle; Canadian lynx data; Time series prediction;

机译：径向基函数网络;状态自回归模型;稳定性分析;极限周期;加拿大山猫数据;时间序列预测;

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1. Stability analysis of RBF network-based state-dependent autoregressive model for nonlinear time series [J] . Min Gan, Hui Peng Applied Soft Computing . 2012,第1期

机译：基于RBF网络的状态相关的非线性时间序列自回归模型的稳定性分析
2. Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model [J] . Garba Inoussa, Hui Peng, Jun Wu Neurocomputing . 2012,第期

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3. Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model [J] . Qin Yemei, Peng Hui, Zhou Feng, Journal of the Franklin Institute . 2015,第10期

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Stability analysis of RBF network-based state-dependent autoregressive model for nonlinear time series

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