Estimating solubilities of ternary water-salt systems using simulated annealing algorithm based generalized regression neural network

Meng, Xianze; Fu, Yunpeng; Yuan, Junsheng

doi:10.1016/j.fluid.2019.112357

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The GRNN is a feed‐forward type of the ANN developed by Specht in 1991 formed by nonlinear functions between the inputs and the outputs 29–31 . The GRNN is a single‐pass learning algorithm and has a less complex structure than MLPNN.…”

Section: Methodsmentioning

confidence: 99%

“…The GRNN is a feed-forward type of the ANN developed by Specht in 1991 formed by nonlinear functions between the inputs and the outputs. [29][30][31] The GRNN is a single-pass learning algorithm and has a less complex structure than MLPNN. Therefore, no iterative procedures are required for the GRNN's training, and there is only one learning parameter for optimization.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

“…Finally, the position of the ants in the population is determined as in Equation (29). After the ant lion eats the Ant i , it updates its position as in Equation (30).…”

Section: Ant Lion Optimizer (Alo)mentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

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

Şenel

2021

Int J Numerical Modelling

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“…GRNN belongs to the category of probabilistic neural network that needs only a fraction of the training samples compared to the algorithms of a Backpropagation-based neural network [ 29 , 31 ]. It is widely applied in the parameter estimation process as can be seen in [ 32 , 33 ] works The network was trained using two acquisitions of the PTP value for each scan, leaving the third acquisition for validation. The GRNN input layer is composed by the PTP values of the frequency scan.…”

Section: Methods Validationmentioning

confidence: 99%

“…The estimated output value

can be viewed as a weighted average of all observed values [ 29 ]. Each output value of the

training samples is weighted exponentially according to the Euclidean distance from the X input sample to the

training sample in order to get the

estimate [ 32 ]. Therefore,

where

is the spreading constant and

is the distance between a training sample (

) for a prediction point ( X ).…”

Section: Methods Validationmentioning

confidence: 99%

Identification and Localization of Track Circuit False Occupancy Failures Based on Frequency Domain Reflectometry

Alvarenga

Cerqueira

Filho

et al. 2020

Sensors

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Railway track circuit failures can cause significant train delays and economic losses. A crucial point of the railway operation system is the corrective maintenance process. During this operation, the railway lines have the circulation of trains interrupted in the respective sector, where traffic restoration occurs only after completing the maintenance process. Depending on the cause and length of the track circuit, identifying and solving the problem may take a long time. A tool that assists in track circuit fault detection during an inspection adds agility and efficiency in its restoration and cost reduction. This paper presents a new method, based on frequency domain reflectometry, to diagnose and locate false occupancy failures of track circuits. Initially, simulations are performed considering simplified track circuit approximations to demonstrate the operation of the proposed method, where the fault position is estimated by identifying the null points and through non-linear regression on signal amplitude response. A field test is then carried out in a track circuit approximately 1500 m long to validate the proposed method. The results show that the proposed method can identify and estimate the fault location due to a short circuit between rails with high accuracy.

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Application of Improved GRNN Algorithm for Task Man-Hours Prediction in Metro Project

Zhang

Wang

2022

Lecture Notes in Electrical Engineering

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Estimating solubilities of ternary water-salt systems using simulated annealing algorithm based generalized regression neural network

Cited by 9 publications

References 12 publications

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

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

Identification and Localization of Track Circuit False Occupancy Failures Based on Frequency Domain Reflectometry

Application of Improved GRNN Algorithm for Task Man-Hours Prediction in Metro Project

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