Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour

Sun, Xudong; Liu, Junbin; Zhu, Ke; Hu, Jun; Jiang, Xiaogang; Liu, Yande

doi:10.1098/rsos.190485

Cited by 21 publications

(13 citation statements)

References 24 publications

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“…Therefore, deep learning classifiers can operate directly on THz training data, enabling faster learning that is much needed in fast-changing THz conditions. We highlight two particular types of supervised neural networks because of their popular usage in existing THz material sensing literature: Generalized regression neural networks (GRNN) [39] and backpropagation neural networks (BPNN) [40].…”

Section: Partial Least Squares-discriminant Analysismentioning

confidence: 99%

“…The most extensively used deep learning model in THz material classification studies is GRNN, both for theoretical and practical applications [39]. GRNN is a typical feedforward neural network that provides a powerful variation to the conventional radial bases function neural network.…”

Section: A Generalized Regression Neural Networkmentioning

confidence: 99%

“…The corresponding performance differs significantly with smoothing factor choice, where the estimated density takes a multivariate Gaussian form with larger smoothing factors. GRNN consists of four layers: Input, pattern, summation, and output [41].…”

Section: A Generalized Regression Neural Networkmentioning

confidence: 99%

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Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Helal¹,

Sarieddeen²,

Dahrouj³

et al. 2021

Preprint

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Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding usecases, and we present few future research directions in the field.

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Section: Partial Least Squares-discriminant Analysismentioning

confidence: 99%

Section: A Generalized Regression Neural Networkmentioning

confidence: 99%

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Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Helal¹,

Sarieddeen²,

Dahrouj³

et al. 2021

Preprint

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show abstract

“…The GRNN is a single-pass learning network with no training parameters while the backpropagation neural network (BPNN) needs forward and backward pass training. The only adjustable parameter in GRNN is the smoothing factor ρ (Sun, Liu, Zhu, Hu, Jiang and Liu, 2019).…”

Section: Proofmentioning

confidence: 99%

Model-plant mismatch learning offset-free model predictive control

Son,

Kim,

et al. 2020

Preprint

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We propose model-plant mismatch learning offset-free model predictive control (MPC), which learns and applies the intrinsic model-plant mismatch, to effectively exploit the advantages of model-based and data-driven control strategies and overcome the limitations of each approach. In this study, the model-plant mismatch map on steady-state manifold in the controlled variable space is approximated via a general regression neural network from the steady-state data for each setpoint. Though the learned model-plant mismatch map can provide the information at the equilibrium point (i.e., setpoint), it cannot provide model-plant mismatch information during the transient state. Moreover, the intrinsic model-plant mismatch can vary due to system characteristics changes during operation. Therefore, we additionally apply a supplementary disturbance variable which is updated from the disturbance estimator based on the nominal offset-free MPC scheme. Then, the combined disturbance signal is applied to the target problem and finite-horizon optimal control problem of offset-free MPC to improve the prediction accuracy and closed-loop performance of the controller. By this, we can exploit both the learned model-plant mismatch information and the stabilizing property of the nominal disturbance estimator approach. The closed-loop simulation results demonstrate that the developed scheme can properly learn the intrinsic model-plant mismatch and efficiently improve the model-plant mismatch compensating performance in offset-free MPC. Moreover, we examine the robust asymptotic stability of the developed offset-free MPC scheme, which is known to be difficult to analyze in nominal offset-free MPC, by exploiting the learned model-plant mismatch information.

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“…There are studies in the literature where the parameters of methods that are used for modeling have been optimized. 14,21,23 In prediction of S and N parameters, Gunes et al 14 carried out prediction by combining the Support Vector Regressor and Convex Optimization methods. They compared the combined method they developed to the GRNN method.…”

mentioning

confidence: 99%

Novel neural network optimization approach for modeling scattering and noise parameters of microwave transistor

Şenel

2021

Int J Numerical Modelling

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This study performed modeling of the scattering (S) and noise (N) parameters of the ATF53189 using the General Regression Neural Network (GRNN) and Multi Layer Perceptron Neural Network (MLPNN) methods based on Artificial Neural Network (ANN). For modeling the linear behavior of the transistor, the optimum design parameters of the GRNN and the MLPNN methods were determined using four different optimization algorithms. These are whale optimization algorithm (WOA), artificial bee colony (ABC), particle swarm optimization (PSO) and ant lion optimizer (ALO) algorithms. With the help of these algorithms, the sigma parameter of the GRNN and the number of hidden layers, numbers of neurons in the hidden layers and the activation functions of the hidden layers of the MLPNN were optimized. This way, the best models required for prediction of the S and N parameters of the ATF53189 were obtained. Different models that provided each of the angles and magnitudes of the S11, S21, S12, S22 parameters and the Fmin, Γmin, Γopt magnitude, Γopt angle and Rn noise parameters as the output were created. The experimental results showed that the GRNN method should be used in linear behavior modeling of S parameters of the ATF53189 and the MLPNN method should be used in linear behavior modeling of N parameters of the ATF53189. It is understood that the best algorithm for optimizing the design parameters of the GRNN and the MLPNN methods is the PSO. As a result, the modeling of the S and N parameters of the ATF53189 transistor was successfully carried out with the methods used in this study.

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Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour

Cited by 21 publications

References 24 publications

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Model-plant mismatch learning offset-free model predictive control

Novel neural network optimization approach for modeling scattering and noise parameters of microwave transistor

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