Area-Efficient Fixed-Width Squarer With Dynamic Error-Compensation Circuit

Chen, Yuan-Ho

doi:10.1109/tcsii.2015.2435752

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Dynamic measurement errors of sensors are difficult to model with traditional mathematics because they have four features [ 5 ]: time-varying, randomness, correlation and dynamic. Because of its complexity, predicting the dynamic errors has been a popular research field [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Jiang

Lan

Jiang

et al. 2018

Sensors

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Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

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Section: Introductionmentioning

confidence: 99%

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Jiang

Lan

Jiang

et al. 2018

Sensors

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show abstract

“…Dynamic measurement errors of sensors are difficult to modeling with traditional mathematics cause they has four features [1]: timevarying, randomness, correlation and dynamic. Because its complexity, predicting the dynamic error has been a popular research fields [2][3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

Jiang¹,

Lan²,

Jiang³

et al. 2017

Preprint

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Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

show abstract