Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network

Zhang, Ru; Duan, Yuanfeng; Yang, Zhao; He, Xuan

doi:10.3390/s18072176

Cited by 48 publications

(32 citation statements)

References 19 publications

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“…The residual errors are back-propagated through the neural network, the Levenberg-Marquardt (LM) algorithm was used to adjust the weights and biases, so as to build the proper compensation model and reduce the residual errors. The LM algorithm is more efficient for training the moderate-sized neural networks, which is up to several hundred weights, with good prediction performance [20,29].…”

Section: Training Of the Bp Neural Networkmentioning

confidence: 99%

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Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm

Jia

Jiang

et al. 2020

Sensors

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The measurement accuracy of the precision instruments that contain rotation joints is influenced significantly by the rotary encoders that are installed in the rotation joints. Apart from the imperfect manufacturing and installation of the rotary encoder, the variations of ambient temperature could cause the angle measurement error of the rotary encoder. According to the characteristics of the 2 π periodicity of the angle measurement at the stationary temperature and the complexity of the effects of ambient temperature changes, the method based on the Fourier expansion-back propagation (BP) neural network optimized by genetic algorithm (FE-GABPNN) is proposed to improve the angle measurement accuracy of the rotary encoder. The proposed method, which innovatively integrates the characteristics of Fourier expansion, the BP neural network and genetic algorithm, has good fitting performance. The rotary encoder that is installed in the rotation joint of the articulated coordinate measuring machine (ACMM) is calibrated by using an autocollimator and a regular optical polygon at ambient temperature ranging from 10 to 40 °C. The contrastive analysis is carried out. The experimental results show that the angle measurement errors decrease remarkably, from 110.2″ to 2.7″ after compensation. The mean root mean square error (RMSE) of the residual errors is 0.85″.

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Section: Training Of the Bp Neural Networkmentioning

confidence: 99%

“…A two-dimensional polynomial fitting method was also proposed for comparison. It was concluded that the BP neural work method was more effective and robust [20]. Araghi et al proposed a temperature-dependent model using the RBF neural network to compensate measurement errors of the micro-electromechanical systems (MEMSs) inertial sensors.…”

Section: Introductionmentioning

confidence: 99%

Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm

Jia

Jiang

et al. 2020

Sensors

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“…Because these analog patterns have a small sample size, they can only be modeled using small sample prediction methods to maximize information utilization. Although Back Propagation (BP) neural networks [35] can be used for small-sample prediction, model training is difficult and prone to failing during the training process. SVM is suitable for processing small samples and nonlinear problems, and has good robustness and high prediction accuracy.…”

Section: Inputmentioning

confidence: 99%

Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

Zha

Song

et al. 2020

Energies

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The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).

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“…ANN is a framework for machine learning inspired by biological neural networks. One of the most widely applied models is back propagation neural network (BPNN) [29]. BPNN is a kind of feed-forward network, the connection weights of which are trained by error back propagation algorithm.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks

Chen

Liu

et al. 2019

Entropy

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The modeling and forecasting of BBS (Bulletin Board System) posts time series is crucial for government agencies, corporations and website operators to monitor public opinion. Accurate prediction of the number of BBS posts will assist government agencies or corporations in making timely decisions and estimating the future number of BBS posts will help website operators to allocate resources to deal with the possible hot events pressure. By combining sample entropy (SampEn) and deep neural networks (DNN), an approach (SampEn-DNN) is proposed for BBS posts time series modeling and forecasting. The main idea of SampEn-DNN is to utilize SampEn to decide the input vectors of DNN with smallest complexity, and DNN to enhance the prediction performance of time series. Selecting Tianya Zatan new posts as the data source, the performances of SampEn-DNN were compared with auto-regressive integrated moving average (ARIMA), seasonal ARIMA, polynomial regression, neural networks, etc. approaches for prediction of the daily number of new posts. From the experimental results, it can be found that the proposed approach SampEn-DNN outperforms the state-of-the-art approaches for BBS posts time series modeling and forecasting.

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Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network

Cited by 48 publications

References 19 publications

Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm

Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm

Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks

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