Blind equalizer for constant-modulus signals based on Gaussian process regression

Hwang, Kyuho; Choi, Sooyong

doi:10.1016/j.sigpro.2011.11.022

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…This is noteworthy that these criteria have been widely used in earlier studies (e.g., Moayedi and Hayati [37], Vakili, et al [55] and Moayedi and Armaghani [56]). Equations (14)- (18) presented the formulation of R 2 , MAE, RMSE, RAE, RRSE, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, overfitting of the network as well as being trapped in local minima can be mentioned as essential drawbacks of ANNs. A regression-based method such as Gaussian processes regression (GPR) and simple linear regression (SLR) have attracted far attention in many research fields such as traffic forecasting [16], tourism demand approximation [17] and signal processing [18]. Similarly, plenty of complex geotechnical problems have been modelled with this approach [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

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In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

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“…Other examples can be found in the following Refs. [19,112,[126][127][128][129][130][131][132][133][134][135].…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

304

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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“…Because the blind balanced algorithm does not need to send the training sequence, which greatly improves the bandwidth utilization. However, its slow convergence rate is slower, and its static error is also bigger [1,2] . Genetic algorithm(GA for brevity) [3][4][5] is an auto-adapted probability search algorithm based on global optimization, has stronger robustness and global random search abilities, and can find global optimal solution in complex, multi-peak, nondifferentiable large vector space effectively and quickly, which makes the possibility of falling into local convergence greatly reduced..…”

Section: Introductionmentioning

confidence: 99%

Multi-modulus blind equalization algorithm based on high-order QAM genetic optimization

Xiao

et al. 2012

2012 8th International Conference on Natural Computation

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A multi-modulus blind equalization algorithm based on genetic algorithm is proposed in this paper. It combines the advantages of multi-mode blind equalization algorithm and decision directed algorithm, uses genetic algorithm to optimize the weight vector of equalizer and gets better balanced results. Theoretical analysis and computer simulations indicate that the proposed algorithm outperforms multi-modulus blind equalization algorithm in convergence rate and steady-state convergence residual.

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Blind equalizer for constant-modulus signals based on Gaussian process regression

Cited by 14 publications

References 13 publications

Predicting Slope Stability Failure through Machine Learning Paradigms

Predicting Slope Stability Failure through Machine Learning Paradigms

A review on prognostic techniques for non-stationary and non-linear rotating systems

Multi-modulus blind equalization algorithm based on high-order QAM genetic optimization

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