Minimizing temperature drift errors of conditioning circuits using artificial neural networks

Pereira, José Miguel; Postolache, Octavian; Girão, Pedro Silva; Cretu, M.

doi:10.1109/19.872941

Cited by 31 publications

(8 citation statements)

References 5 publications

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“…The modeling of the sensor has been reported elsewhere [17]. ANN-based software techniques have been established as an effective means for compensating nonlinearity, temperature and hysteresis for humidity, pressure and temperature sensors [14][15][16][17][18][19]. However, an integrated ANN-based scheme incorporating hysteresis and nonlinearity compensations of nanocrystalline porous siliconbased humidity sensors have not been reported so far.…”

Section: Introductionmentioning

confidence: 98%

ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Islam

Ghosh

Saha

2006

Sensors and Actuators B: Chemical

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Section: Introductionmentioning

confidence: 98%

ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Islam

Ghosh

Saha

2006

Sensors and Actuators B: Chemical

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“…1 Instead of employing temperature compensating resistors, digital compensation may be performed by generating a thermal zero shift signal using preprogramed temperature-induced zero shift correction data, 2 and novel temperature compensation methods have been proposed including data fusion based on artificial neural networks 3,4 and adaptive noise canceling by bridge output voltage-to-frequency conversion. 5 The zero point of a force transducer tends to shift in response to changes in temperature.…”

Section: Introductionmentioning

confidence: 99%

Temperature Dependence of Zero Point in Force Transducers II: How to Account for the Effects of a Zero Balancing Resistor

Yi¹,

Kim

2013

Exp Techniques

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On the basis of findings discussed in a companion paper, two novel calculation procedures (iterative and analytic) for force transducer zero balancing without inducing temperature errors in the Wheatstone bridge circuit are introduced. The iterative method calculates a proper temperature compensating resistance without accounting for the effects of a zero balancing resistor and then determines the resistance of a zero balancing resistor. This process is repeated by computing subsequent ancillary resistance pairs until the thermal zero shift is small enough. The analytic solution uses bridge output ratio expressions at two different temperatures in the presence of a temperature compensating resistor and a zero balancing resistor in terms of the output ratios at the two temperatures in the absence of both resistors. It is found that the difference in output ratio between the two temperatures obtained by using the iterative method converges to zero as long as the temperature compensating resistor TCR is greater than the zero balancing resistor TCR. It is also found that the iterative solution is practically equivalent to the analytic solution in that the total resistance of temperature compensating resistors obtained from using the iterative method converges to the temperature compensating resistance calculated using the analytic solution.

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“…One of the most powerful uses of neural networks is in function approximation (curve fitting). In this context, the usage of neural network techniques provides lower interpolation errors when compared with classical method of polynomial interpolation [5].…”

Section: Introductionmentioning

confidence: 99%

“…When small amplitude signals from transducer are considered and environmental conditions of conditioning circuits exhibit a large temperature range, the temperature drift errors have a real impact in system accuracy. Neural networks based solution to overcome the problem of temperature drift errors of signal conditioning circuits has been proposed by [5]. Patra and Bos [7], and Pramanik et al [8] have proposed a novel computationally efficient neural network for modeling of a pressure sensor operated in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Design of Near-Optimal Classifier Using Multi-Layer Perceptron Neural Networks for Intelligent Sensors

Charniya¹

2013

IJMO

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Abstract-The Multi-layer Perceptron Neural Networks (MLP NN) are well known for their simplicity, ease of training for small-scale problems, and suitability for online implementation. This paper presents the methodology and challenges in the design of near-optimal MLP NN based classifier with maximize classification accuracy under the constraints of minimum network dimension for implementation intelligent sensors.Index Terms-Classifier, neural networks, multi-layer percptron, intelligent sensors.

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Minimizing temperature drift errors of conditioning circuits using artificial neural networks

Cited by 31 publications

References 5 publications

ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Temperature Dependence of Zero Point in Force Transducers II: How to Account for the Effects of a Zero Balancing Resistor

Design of Near-Optimal Classifier Using Multi-Layer Perceptron Neural Networks for Intelligent Sensors

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