A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling

Authors:
Min Gan;Hui Peng;Xiaoyan Peng;Xiaohong Chen;Garba Inoussa
Affiliations:
School of Information Science and Engineering, Central South University, Changsha 410083, China;School of Information Science and Engineering, Central South University, Changsha 410083, China;College of Mechanical and Automobile Engineering, Hunan University, Changsha 410082, China;School of Business, Central South University, Changsha 410083, China;School of Information Science and Engineering, Central South University, Changsha 410083, China
Venue:
Information Sciences: an International Journal
Year:
2010

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Abstract

This paper presents a modeling approach to nonlinear time series that uses a set of locally linear radial basis function networks (LLRBFNs) to approximate the functional coefficients of the state-dependent autoregressive (SD-AR) model. The resulting model, called the locally linear radial basis function network-based autoregressive (LLRBF-AR) model, combines the advantages of the LLRBFN in function approximation and of the SD-AR model in nonlinear dynamics description. The LLRBFN weights that connect the hidden units with the output are linear functions of the input variables; this differs from the conventional RBF network weight structure. A structured nonlinear parameter optimization method (SNPOM) is applied to estimate the LLRBF-AR model parameters. Case studies on various time series and chaotic systems show that the LLRBF-AR modeling approach exhibits much better prediction accuracy compared to some other existing methods.