Neural network signal understanding for instrumentation

We propose a novel computationally efficient artificial neural network (NN) for design and development of intelligent sensors to operate in harsh environments which can have wide variation of environmental conditions. The proposed Hermite NN (HeNN) models the inverse characteristics of a sensor and can provide linearized sensor response characteristics irrespective of change in environmental conditions, even wh~n the environmental parameters influence the sensor characteristics nonlinearly. By taking an example of a capacitive pressure sensor, we have shown through extensive computer simulations that the HeNN-based model can linearize its response with maximum full scale (FS) error of ±0.5% when it is operated in a harsh environment with temperature variation of -50 to 200 0 C and influenced nonlinearly. We have compared performance of the proposed HeNN-based model with a MLP-based model and shown its superior performance in terms of FS error and computational complexity.

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“…(1) 7 is a sensitivity parameter that depends on the physical geometry and material properties of the CPS.…”

Section: -Nmentioning

confidence: 99%

“…Some of the early applications of NN in instrumentation and measurement can be found in [7]- [11]. Application of NNs with superior performance for nonlinearity estimation in pressure sensor [12], compensation for environmental dependency and nonlinearities of sensor characteristics in pressure sensors [13]- [14] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Hermite neural network-based intelligent sensors for harsh environments

Patra

Bornand

Chakraborty

2009

2009 International Joint Conference on Neural Networks

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“…The wide applicability of NNs stems from their flexibility and ability to model non-linear systems without prior knowledge of an empirical model. This gives ANNs an advantage over traditional fitting methods for instrumentation and measurement (Pau and Johansen 1990) and industrial processes (Haykin 1998). The availability of an input-output ANN model for a complex process allows the prediction of the system behavior.…”

Section: Introductionmentioning

confidence: 99%

Temperature compensation of micromachined silicon hot wire sensor using ANN technique

et al. 2012

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Temperature is one of the most important factors influencing accurate silicon sensor devices and is one of the largest sources of error in measurement. In this paper, a model based on Neural Networks (NN), has been implemented to generate fluid velocity data, knowing fluid temperature measurements. The proposed model based on neural networks can provide the calibrated response characteristics irrespective of change in the sensor characteristics due to change in ambient temperature. The NN-based sensor model automatically calibrates and compensates with high accuracy for the nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental parameters. Through extensive simulated experiments, we have shown that the NN-based silicon hot wire sensor model can provide flow speed readout with a maximum full-scale error of only 1.5% over a temperature range from 0 to 40°C for nonlinear dependencies.

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“…These networks are endowed with certain unique characteristics such as the capability of universal approximation, generalization, and fault tolerance. Because of these characteristics, there have been numerous successful applications of NNs in various fields of science, engineering, and industry [8,9,10]. It has been shown that the NN-based approximations to measurement data perform better than those of the classical methods of data interpolation and least mean square regression [11].…”

Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

Patra

Ang

Chaudhari

et al. 2005

EURASIP J. Adv. Signal Process.

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We present an artificial neural-network-(NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to 250• C. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only ±1.0% over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit-(MCU-) based implementation scheme is also provided.

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Neural network signal understanding for instrumentation

Cited by 34 publications

References 5 publications

Hermite neural network-based intelligent sensors for harsh environments

Hermite neural network-based intelligent sensors for harsh environments

Temperature compensation of micromachined silicon hot wire sensor using ANN technique

Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

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