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Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

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ABSTRACT

The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems ideally should spend the least possible amount of time in their calibration. An autocalibration algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity, as accurately as possible. This paper describes a new autocalibration methodology for nonlinear intelligent sensors based on artificial neural networks, ANN. The methodology involves analysis of several network topologies and training algorithms. The proposed method was compared against the piecewise and polynomial linearization methods. Method comparison was achieved using different number of calibration points, and several nonlinear levels of the input signal. This paper also shows that the proposed method turned out to have a better overall accuracy than the other two methods. Besides, experimentation results and analysis of the complete study, the paper describes the implementation of the ANN in a microcontroller unit, MCU. In order to illustrate the method capability to build autocalibration and reconfigurable systems, a temperature measurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

No MeSH data available.


Performance of the ANN to self-calibration. a) With five calibration points, b) with six calibration points c) with seven calibration points and d) with eight calibration points.
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f3-sensors-07-01509: Performance of the ANN to self-calibration. a) With five calibration points, b) with six calibration points c) with seven calibration points and d) with eight calibration points.

Mentions: The ANN training was made with five to eight calibration points using the function , normalized with the equations (3) and (4). τ represents the percentage of the nolinearity of x. The artificial neural network was tested with different level of nonlinear input signals. The proposed method was compared against the piecewise [35] and polynomial linearization methods [36] using simulation software. Figure 3 illustrates the results of the comparison of the ANN, piecewise and polynomial methods. Each figure shows the performance of these methods for different calibration points regarding different levels of input nonlinearity, from 10% to 65%.


Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks
Performance of the ANN to self-calibration. a) With five calibration points, b) with six calibration points c) with seven calibration points and d) with eight calibration points.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814866&req=5

f3-sensors-07-01509: Performance of the ANN to self-calibration. a) With five calibration points, b) with six calibration points c) with seven calibration points and d) with eight calibration points.
Mentions: The ANN training was made with five to eight calibration points using the function , normalized with the equations (3) and (4). τ represents the percentage of the nolinearity of x. The artificial neural network was tested with different level of nonlinear input signals. The proposed method was compared against the piecewise [35] and polynomial linearization methods [36] using simulation software. Figure 3 illustrates the results of the comparison of the ANN, piecewise and polynomial methods. Each figure shows the performance of these methods for different calibration points regarding different levels of input nonlinearity, from 10% to 65%.

View Article: PubMed Central

ABSTRACT

The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems ideally should spend the least possible amount of time in their calibration. An autocalibration algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity, as accurately as possible. This paper describes a new autocalibration methodology for nonlinear intelligent sensors based on artificial neural networks, ANN. The methodology involves analysis of several network topologies and training algorithms. The proposed method was compared against the piecewise and polynomial linearization methods. Method comparison was achieved using different number of calibration points, and several nonlinear levels of the input signal. This paper also shows that the proposed method turned out to have a better overall accuracy than the other two methods. Besides, experimentation results and analysis of the complete study, the paper describes the implementation of the ANN in a microcontroller unit, MCU. In order to illustrate the method capability to build autocalibration and reconfigurable systems, a temperature measurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

No MeSH data available.