Real-time correction for sensor's dynamic error based on DSP

Wu, Jian; Zhang, Zhijie; Dong, Ganggang; Wang, Wenlian

doi:10.1109/imtc.2011.5944126

Cited by 3 publications

(3 citation statements)

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“…The dynamic compensator is normally of high order, and the dynamic errors of the sensor are difficult to amend quickly and in real time according to the programming ideas of the traditional displacement summation [3]. The current study establishes a parallel method to develop hardware for dynamic compensation filtering according to the concepts of the distributed algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of the aforementioned principles, the present paper proposes an improved PSO algorithm that employs adaptive neural networks to determine the optimal initial value for each particle in the particle swarm within the shortest time. This algorithm eventually yields a holistic optimal value.The dynamic compensator is normally of high order, and the dynamic errors of the sensor are difficult to amend quickly and in real time according to the programming ideas of the traditional displacement summation [3]. The current study establishes a parallel method to develop hardware for dynamic compensation filtering according to the concepts of the distributed algorithm.…”

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confidence: 99%

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Online Correction of the Dynamic Errors in a Stored Overpressure Measurement System

Wang

Zhang

2015

TELKOMNIKA

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The problem encountered in sharp shock testing as a result of inadequate bandwidth must be addressed to obtain IntroductionA dynamic testing system is installed underground to test explosion shockwaves. A sensor interface is located above ground. The spectrum of the pressure sensor cannot cover the signal spectrum under the influence of the cliff front of the shockwave. Thus, signal amplitude increases significantly. The areas selected by the dynamic testing system are shocked sharply, and peak overpressure cannot be determined accurately when the sensor approaches resonant frequency. Hence, the amplitude frequency characteristics of a piezoelectric sensor should be compensated. Compensation methods include inverse filtering, the collocation of zero poles, and system identification [1]. In addition, neural network, particle swarm optimization (PSO) algorithm, and other algorithms are adopted to improve compensation precision.Neural networks can easily be trapped in the local minimum regardless of network search speed, and Internet precision is difficult to improve at latter stages of training. Although the PSO algorithm is a holistic optimal algorithm, the initial position of the particle affects the optimization results of this algorithm [2]. On the basis of the aforementioned principles, the present paper proposes an improved PSO algorithm that employs adaptive neural networks to determine the optimal initial value for each particle in the particle swarm within the shortest time. This algorithm eventually yields a holistic optimal value.The dynamic compensator is normally of high order, and the dynamic errors of the sensor are difficult to amend quickly and in real time according to the programming ideas of the traditional displacement summation [3]. The current study establishes a parallel method to develop hardware for dynamic compensation filtering according to the concepts of the distributed algorithm. This method converts the index of optimal dynamic compensation filtering as obtained with the improved PSO algorithm into the ROM look-up table operation while avoiding the multiplication operation. Compensation results can be generated through the performance of a simple data addition operation after the look-up table is introduced. This process significantly increases operation speed. Finally, this method can effectively measure dynamic compensators in real time.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Online Correction of the Dynamic Errors in a Stored Overpressure Measurement System

Wang

Zhang

2015

TELKOMNIKA

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“…ANN as "universal approximators" [12,13,14] have been widely used for transducer static error correction [15,16,17,18], in particular for transducer and measuring instrument calibration [19,20,21]. Nevertheless, in the field of realtime dynamic error correction, solutions using DSP [22,23,24], FPGA technique [25] and analog circuits [26,27] are dominant. The typical use of ANN is presented, e.g., in [11,28,29].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Real-time Correction of Transducer Dynamic Errors

Roj¹

2013

Measurement Science Review

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In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.

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