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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.


Flow chart of the LMBP algorithm.
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f2-sensors-07-01509: Flow chart of the LMBP algorithm.

Mentions: For this case: If the result of MSE is smaller than computed in the step 1, εk+1,mse < εk,mse, then evaluate μk+1 as , c is a constant value, and continue with the step 2. If the sum of squares is not reduced, εk+1,mse > εk, mse evaluate as μk+1 = μkc and go to the step 2. All this will be repeated until the desired error or k's cycles are reached. Note that the initial values of μ and c are the key to the right convergence, the recommend values are μ =0.01 and c=5. Figure 2 shows the flow chart of the LBMP algorithm.


Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks
Flow chart of the LMBP algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-07-01509: Flow chart of the LMBP algorithm.
Mentions: For this case: If the result of MSE is smaller than computed in the step 1, εk+1,mse < εk,mse, then evaluate μk+1 as , c is a constant value, and continue with the step 2. If the sum of squares is not reduced, εk+1,mse > εk, mse evaluate as μk+1 = μkc and go to the step 2. All this will be repeated until the desired error or k's cycles are reached. Note that the initial values of μ and c are the key to the right convergence, the recommend values are μ =0.01 and c=5. Figure 2 shows the flow chart of the LBMP algorithm.

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.