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

Abstract

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 Sensors 2007, 7 1510 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.

Temperature compensation of micromachined silicon hot wire sensor using ANN technique

“…In order to correct the dependence of ambient temperature and the non linearity of the response of the SHWS, we propose to design a component with neural networks called INV-ANN (Inverse ANN) placed in series with the (Rivera et al 2007). By analogy with the first test, the temperature is varied in the range 15-35°C and the velocity in the range 0-35 m/s.…”

“…The weights and biases of the ANN are determined by training data. The training of the network is performed by the Levenberg-Marquardt Back Propagation Algorithm which is proven to be the most powerful in this application (Cybenco 1989;Rivera et al 2007). This algorithm aims to minimize the global error given by, …”

Radiation-Hard and Intelligent Optical Fiber Sensors for Nuclear Power Plants

“…The advanced fiber optic sensing technologies that could be used for the in fusion reactors, for example ITER, safety monitoring. (Rivera J., et al, 2007). A traditional NPPs control system has almost no knowledge memory.…”

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

“…Today designers have different options for self readjustment sensors, some of which are artificial neural networks theory [16, 24] or recursive algorithms [25-26]…”