Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

Lü, Xiaohong; Wang, Yongquan; Li, Jie; Zhou, Yang; Ren, Zongjin; Liang, Steven Y.

doi:10.1108/ec-09-2018-0410

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…Support Vector Machine (SVM) and BPNN, as the representatives of machine learning, are widely used in various fields and have achieved good results. Lu et al (2019) applied BPNN to prediction of three-dimensional coordinates of space points with simple structure and high precision, and Jiang (2019) used BPNN to estimate the building cost, which had smaller error and converged at about 85 times. Li and Jing (2015) established support vector regression (SVR) model, which was trained between 2D feature size and the corresponding circumference size, to provide the accurate data to dress industry, and Cheng et al (2017) used the audio signals based on the support vector machine (SVM) algorithm to solve the problem of activity analysis of construction equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Tian

Song

et al. 2021

ECAM

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PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R2) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R2) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

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Section: Literature Reviewmentioning

confidence: 99%

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Tian

Song

et al. 2021

ECAM

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“…As a GA is a global search algorithm that can find the global optimal solution, this can address the deficiency of BP neural networks that have a tendency to converge to sub-optimal solutions. Hence, this paper uses a GA to optimize the BP neural network to ensure the accuracy of the model prediction results [14].…”

Section: Bp Neural Network Optimazation With Gamentioning

confidence: 99%

Application of Grey Prediction in a GA-BP Power Theft Algorithm

Chen¹,

Xu²,

Zhang³

et al. 2020

Fuzzy Systems and Data Mining VI

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Statistics show that power theft is one of the main reasons for the dramatic increase in power grid line loss. In this paper, a genetic algorithm is used to optimize a neural network and establish a power theft prediction model. With the grey prediction model, the predicted values of variables are obtained and then applied to the prediction model of a GA-BP neural network to obtain relatively accurate predictions from limited samples, reducing the absolute error. Through the two levels of prediction and analysis, the model is demonstrated to have good universality in predicting power theft behavior, and is a practical and effective method for power companies to carry out power theft analysis.

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“…Neural network has been widely considered and studied in the field of temperature compensation because of its strong generalization ability, good fault tolerance and strong nonlinear mapping ability. In this paper, a polynomial regression algorithm and BP neural network are proposed to realize the temperature compensation of the pressure sensor; however, the BP neural network has some shortcomings, such as a slow learning rate and susceptibility to falling into the local minimum [ 28 , 29 ]. Therefore, a genetic algorithm (GA) can be used to optimize the shortcomings of the BP neural network before the BP network training parameters [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach

Yang

Yue

Hou

et al. 2022

Sensors

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Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10−2/°C to 1.896 × 10−4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.

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Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

Cited by 15 publications

References 9 publications

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Application of Grey Prediction in a GA-BP Power Theft Algorithm

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach

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