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Estimation of ARMA Model Order Using Artificial Neural Networks

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Abstract

This article presents a novel algorithm for solving the problem of autoregressive moving average (ARMA) model order estimation. The proposed algorithm is based on modeling and designing the artificial neural network (ANN) architecture for a special matrix constructed from the minimum eigenvalue (MEV) criterion. The MEV criterion is based on a covariance matrix derived from the observed output data only. The input signal is unobservable. The proposed ANN-based algorithm is developed by training the MEV dataset using the backpropagation (BP) learning algorithm to select the appropriate ARMA model order. The algorithm uses one-class-one-network (OCON) topology. Hence, the subneural network was developed for each ordered pair. Then, these subnetworks were assembled in one simulator. The ANN-based algorithm was tested on several simulated examples to estimate the ARMA model orders. MEV efficiency was compared with the proposed ANN approach at various signal-to-noise ratios to demonstrate substantial improvements.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request

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Authors and Affiliations

  1. Department of Cyber Security, Faculty of Information Technology, Zarqa University, Zarqa, Jordan

    Khaled E. Alqawasmi

  2. Department of Electronic Engineering, Hijjawi college for Engineering Technology, Yarmouk University, Irbid, Jordan

    Adnan M. Alsmadi

Authors
  1. Khaled E. Alqawasmi
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  2. Adnan M. Alsmadi
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Correspondence to Khaled E. Alqawasmi.

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Alqawasmi, K.E., Alsmadi, A.M. Estimation of ARMA Model Order Using Artificial Neural Networks. Circuits Syst Signal Process 42, 4129–4147 (2023). https://doi.org/10.1007/s00034-023-02305-6

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