Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation

Candan, Çağatay

doi:10.1016/j.sigpro.2019.107256

Cited by 18 publications

(3 citation statements)

References 18 publications

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“…Usually, autocorrelation is used to predict the economic phenomena associated with the previous period. For economic phenomena that are greatly affected by social factors, other variables need to be added, or other methods need to be combined to complete the prediction [23,24].…”

Section: Armmentioning

confidence: 99%

Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Huang

Suhaim

et al. 2021

Applied Mathematics and Nonlinear Sciences

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To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.

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Section: Armmentioning

confidence: 99%

Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Huang

Suhaim

et al. 2021

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…In the area of system identification, much attention has been concentrated on linear systems, nonlinear systems, bilinear systems and so on. Due to the simple system structure, the parameter estimation methods have been matured for linear systems, such as the recursive identification, 10 the iterative estimation, 11,12 the subspace identification, 13 and the maximum likelihood identification 14‐16 . However, nonlinear systems have the features of diverse structures, random interferences, and great uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the simple system structure, the parameter estimation methods have been matured for linear systems, such as the recursive identification, 10 the iterative estimation, 11,12 the subspace identification, 13 and the maximum likelihood identification. [14][15][16] However, nonlinear systems have the features of diverse structures, random interferences, and great uncertainties. These factors make their identification problems more complex and significant.…”

Section: Introductionmentioning

confidence: 99%

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Wei

Xiao

et al. 2022

Intl J Robust & Nonlinear

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This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.

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A Method of Real-Time Dynamic Measurement of a Speaker’s Emotional State from a Speech Waveform

Savchenko

2021

Meas Tech

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The problems of implementing systems with a voice interface for remote service of the population are examined. The effectiveness of such systems can be enhanced by automatic analysis of the changes of the emotional state of the user during dialogue. In order to do real-time measurements of the index of the dynamics of the emotional state, it is proposed to use the effect of the sound (phonetic) variability of speech of the user at observation intervals that are of small duration (fractions of a minute). Based on an information-theoretic approach, a method was developed for acoustic measurements of the dynamics of the emotional state under conditions of small samples, using a scale-invariant measure of the variations of the speech waveform in the frequency domain. An example of the practical instantiation of this method in real-time conditions is examined. It is shown that in this case the delay in obtaining measurement results does not exceed 10-20 s. The results of experimental studies confi rmed the rapid response of the proposed method and its sensitivity to modifi cations of the dynamics of the emotional state under the effect of external perturbations. The developed method can be used to introduce automated monitoring of the quality of voice samples of users of the unifi ed biometric systems. Also, the method will be useful to enhance security by noncontact detection of potentially dangerous persons with short-term disturbance of the psychoemotional state.

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Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation

Cited by 18 publications

References 18 publications

Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

A Method of Real-Time Dynamic Measurement of a Speaker’s Emotional State from a Speech Waveform

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