Sensor linearization with neural networks

Marques, Nicolas; Martin-del-Brio, B.

doi:10.1109/41.969414

Cited by 47 publications

(13 citation statements)

References 3 publications

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“…It is increasingly being recognized that neural network (NN)-based compensation algorithms are widely used in linearizing the nonlinearity of many sensors and devices. Medrano-Marques et al in [ 1 ] use a piecewise-linear function to approximate the nonlinear activation function of the hidden neurons and get a very similar result to those achieved with the original nonlinear activation function. Then, the NN-based model is suited for being programmed into system memory, but the precision is limited.…”

Section: Introductionmentioning

confidence: 81%

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

Yan

Deng

Sun

et al. 2014

Sensors

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In this study, a neural network-based square root of descending (SRD) order decomposition algorithm for compensating for nonlinear data generated by sensors is presented. The study aims at exploring the optimized decomposition of data 1.00,0.00,0.00 and minimizing the computational complexity and memory space of the training process. A linear decomposition algorithm, which automatically finds the optimal decomposition of N subparts and reduces the training time to 1N and memory cost to 1N, has been implemented on nonlinear data obtained from an encoder. Particular focus is given to the theoretical access of estimating the numbers of hidden nodes and the precision of varying the decomposition method. Numerical experiments are designed to evaluate the effect of this algorithm. Moreover, a designed device for angular sensor calibration is presented. We conduct an experiment that samples the data of an encoder and compensates for the nonlinearity of the encoder to testify this novel algorithm.

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Section: Introductionmentioning

confidence: 81%

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

Yan

Deng

Sun

et al. 2014

Sensors

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“…thermistor linearisation using ANN have been reported [20,21]. The uses of ANN motivate the necessity of developing a softwarehardware technique for thermistor linearisation with practical implementation.…”

Section: Research Articlementioning

confidence: 99%

Development of thermistor signal conditioning circuit using artificial neural networks

Kumar

Narayana

2015

IET Science, Measurement & Technology

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“…In particular, Artificial Neural Networks (ANN) constitute the most powerful solution, as they are able to adapt their input–output characteristics without previous knowledge of the particular sensor response [9,10,11,12,13,14,15,16,17]. In this approach, training algorithms are used where sensor input-conditioned output data pairs, which represent the expected input–output characteristic, are iteratively fed to the system, so that the ANN-free parameters (called weights) are adjusted until a maximum permissible error between the expected and the actual output is reached [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks

Martínez-Nieto

Marques

Sanz-Pascual

et al. 2019

Sensors

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For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.

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Sensor linearization with neural networks

Cited by 47 publications

References 3 publications

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

Development of thermistor signal conditioning circuit using artificial neural networks

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks

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