13 October 2008 Nonlinear calibration for petroleum water content measurement using PSO
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To improve the measurement precision of the capacitance method for petroleum water content, this paper presents a nonlinear calibration technique based on neural networks. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a combination algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to train the neural network. It will enable the calibration process with an overall accuracy and a higher converging speed. Simulation results show that this method can effectively eliminate the impact of non-target parameters to the sensor output and has certain project value.
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Mingbao Li, Jiawei Zhang, "Nonlinear calibration for petroleum water content measurement using PSO", Proc. SPIE 7129, Seventh International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration, 71291W (13 October 2008); doi: 10.1117/12.807644; https://doi.org/10.1117/12.807644

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