Gradient iterative-based kernel method for exponential autoregressive models
by Jianwei Lu
International Journal of Computer Applications in Technology (IJCAT), Vol. 67, No. 2/3, 2021

Abstract: Two kernel method-based gradient iterative algorithms are proposed for exponential autoregressive (ExpAR) models in this study. A polynomial kernel function is utilised to transform the ExpAR model into a linear-parameter model. Since the order of the linear-parameter model is large, a momentum stochastic gradient algorithm and an adaptive step-length gradient iterative algorithm are developed. Both these two algorithms can estimate the parameters with less computational efforts. Finally, a simulation example shows that the proposed algorithms are effective.

Online publication date: Thu, 17-Mar-2022

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