Digital instruments recognition based on PCA-BP neural network

Zhang, Jun; Zuo, Lin; Gao, Jiawei; Zhao, Shaoan

doi:10.1109/itnec.2017.8284872

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Therefore, we can regard the trajectory as a multivariate time series. The BP neural network is one of the most widely used and matured artificial neural networks [9,21]. It is a multi-layer feedforward network trained by the error BP algorithm.…”

Section: D Trajectory Prediction Model Based On the Bp Networkmentioning

confidence: 99%

A 4D Trajectory Prediction Model Based on the BP Neural Network

Tian

2019

Journal of Intelligent Systems

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Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

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Section: D Trajectory Prediction Model Based On the Bp Networkmentioning

confidence: 99%

A 4D Trajectory Prediction Model Based on the BP Neural Network

Tian

2019

Journal of Intelligent Systems

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“…Besides, in a study by Zhang (2017) in recognizing and predicting mental disease based on 10000 datasets, the best mean square error value was recorded when the number of hidden nodes increased from eight to 16 nodes. Furthermore, in a study by Zhang, Zuo, Gao, and Zhao (2017) to recognize the image of 1000 digital instruments, they found that 18 hidden nodes was the optimal number of hidden nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Ahmarofi

Ramli

Abidin

et al. 2019

Journal of Information and Communication Technology

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Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line.

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Extended Kalman Filter Method based on Backpropagation Neural Network in Current Sensor Online Calibration

Zhao

Zhang

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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The signal collected by current sensor will contain various noises, which will have a negative impact on the its accuracy and calibration. In order to remove unwanted noises, an extended Kalman filter (EKF) method based on backpropagation (BP) neural network is proposed in this paper. BP neural network has good adaptive ability and non-linear mapping ability. EKF can effectively filter noise and improve the calibration accuracy for non-linear systems. The signal collected by current sensor is processed by EKF and is used as the input signal of BP neural network. The trained neural network can modify the output signal of EKF, so as to improve the calibration accuracy. The angle difference and ratio difference of current sensor calibration are below 0.1, which meets the national standard and shows the effectiveness of this method.

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Digital instruments recognition based on PCA-BP neural network

Cited by 7 publications

References 4 publications

A 4D Trajectory Prediction Model Based on the BP Neural Network

A 4D Trajectory Prediction Model Based on the BP Neural Network

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Extended Kalman Filter Method based on Backpropagation Neural Network in Current Sensor Online Calibration

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