Title: Gradient iterative-based kernel method for exponential autoregressive models

Authors: Jianwei Lu

Addresses: School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou 213164, Jiangsu, China

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.

Keywords: ExpAR model; kernel method; linear-parameter model; momentum stochastic gradient algorithm; adaptive step-length; gradient iterative algorithm.

DOI: 10.1504/IJCAT.2021.10045757

International Journal of Computer Applications in Technology, 2021 Vol.67 No.2/3, pp.224 - 231

Received: 03 Nov 2020
Accepted: 02 Jan 2021

Published online: 17 Mar 2022 *

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