Back Propagation Neural Network Model for Temperature and Humidity Compensation of a Non Dispersive Infrared Methane Sensor

Wang, Hairong; Zhang, Wei; You, Liudong; Yuan, Guoying; Zhao, Yulong; Jiang, Zhuangde

doi:10.1080/10739149.2013.816965

Cited by 26 publications

(9 citation statements)

References 9 publications

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“…The analytical approaches such as look-up table, interpolation and surface fitting [6]- [8] are easy to be implemented in sensor circuits while it may confront two kinds of dilemma: the number of interpolation nodes dramatically increases with the requirement of measurement precision and the ill-conditioned problem in solving normal equations when the fitting order increases. The artificial intelligence approaches involve BP neural networks [9]- [11] and support vector machine [12]- [14]. The empirical risk minimum (ERM) principle and gradient descent iteration are the cornerstones of BP neural networks, which may lead the modeling process fall into some pitfalls as the curse of dimensionality, local minimum, under-fitting or over-fitting, etc., [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Zhang

et al. 2020

IEEE Access

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As the silicon material is severely influenced by the ambient temperature, the silicon piezo-resistive pressure sensor remarkably suffers from a strong nonlinearity in the response characteristic as the ambient temperature varies. To address this crucial issue, an adaptive mutation particle swarm optimization optimized support vector regression (AMPSO-SVR) combined with improved AdaBoost.RT algorithm is presented. The opposition-based learning initialization and Levy mutation is applied in the adaptive mutation particle swarm optimization (AMPSO) to achieve the appropriate model selection task which directly determines the performance of SVR. The performance of original AdaBoost.RT is improved by a dynamical modification approach for threshold and quoted error criterion. In order to verify the effectiveness of the proposed temperature compensation approach, several additional optimization methods such as Cuckoo search (CS), dragonfly algorithm (DA), multi-verse optimizer (MVO), conventional particle swarm optimization (PSO), Levy flight improved particle swarm optimization (Levy-PSO) and the AMPSO combined with SVR are investigated. The minimum quoted error, maximum quoted error, the mean quoted error and the variance of the quoted error over testing data obtained by the proposed method are 6.8764×10 −5 , 6.4463×10 −4 , 3.2619×10 −4 and 2.5714×10 −8 respectively, which are superior to the corresponding indices obtained by other methods. The analysis of simulation results indicates the method proposed in this research is applicable, effective and efficient for industrial application.

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Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Zhang

et al. 2020

IEEE Access

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“…In recent years, neural networks have many applications in sensor signal processing, nonlinear correction, temperature compensation, and so on. BP (back propagation) neural network model has been brought into infrared temperature and humidity compensation [3,4]. RBF (Radial Basis Function) neural network is applied to precision motion system [5] and neural network is used in the pressure analysis [6] and gas concentration measurement [7] in industrial environment; and a new method of Correction of Dynamic Errors of a Gas Sensor Based on Neural Network has been presented [8], etc.…”

Section: Introductionmentioning

confidence: 99%

“…T refers to unit time on the same computer 2. Average relative error is the average value of the relative error of all test data 3. Maximum relative error is the maximum value of the relative error of all test data.…”

mentioning

confidence: 99%

Precision Improvement for the Detection of TGC via RBF Network

Yan

2020

Shock and Vibration

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The approach of multipoint measurement, with increasing hardware cost, should no longer be adopted against the problem of low detection precision on the quality and concentration measurement of large-caliber or irregular pipeline gas with the single platinum film probe. Alternatively, the data correction and improvement can be carried out through establishing an RBF model to detect sample gas after preprocessing. Furthermore, the computer simulation and error analysis can be implemented by taking actual SO2 data emitted by one medium-sized coal-fired power plant in China as a training sample. Hence, it can be shown that this approach on improvement and analysis of continuous monitoring of the systematic integrated error against the instrument correction and flue gas emission has feasibility, and the comprehensive average error is less than 0.6%.

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“…On the other hand, their vigorous algorithm robustness and fault tolerance ability has led researchers to focus a great deal of attention on artificial intelligence compensation approaches. The most up-to-date software compensation methods are rooted in artificial intelligence include neural networks [ 16 , 17 , 18 , 19 ], support vector machines (SVM) and least squares support vector machine (LSSVM) [ 20 , 21 , 22 ]. The classic back-propagation neural networks may suffer from the dimensionality curse, local minima, under-fitting or over-fitting, etc.…”

Section: Introductionmentioning

confidence: 99%

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

Zhou³

et al. 2017

Sensors

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As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

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Back Propagation Neural Network Model for Temperature and Humidity Compensation of a Non Dispersive Infrared Methane Sensor

Cited by 26 publications

References 9 publications

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Precision Improvement for the Detection of TGC via RBF Network

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

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