A novel prediction method for down-hole working conditions of the beam pumping unit based on 8-directions chain codes and online sequential extreme learning machine

2018 ◽  
Vol 160 ◽  
pp. 285-301 ◽  
Author(s):  
Kun Li ◽  
Ying Han ◽  
Tong Wang
Keyword(s):  
Extreme Learning Machine ◽  
Working Conditions ◽  
Prediction Method ◽  
Chain Codes ◽  
Pumping Unit ◽  
Learning Machine

scholarly journals Prediction method of flow instability based on multi-objective optimized extreme learning machine

2014 ◽  
Vol 63 (20) ◽  
pp. 200505
Author(s):  
Chen Han-Ying ◽  
Gao Pu-Zhen ◽  
Tan Si-Chao ◽  
Fu Xue-Kuan
Keyword(s):  
Extreme Learning Machine ◽  
Flow Instability ◽  
Prediction Method ◽  
Multi Objective ◽  
Learning Machine

scholarly journals Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

2020 ◽  
Vol 309 ◽  
pp. 04018
Author(s):  
Guangjie Hao ◽  
Menghong Yu ◽  
Zhen Su
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Prediction Method ◽  
Bat Algorithm ◽  
Yangtze Estuary ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Density Prediction ◽  
Learning Machine ◽  
Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

scholarly journals Path Planning of Hydraulic Support Pushing Mechanism Based on Extreme Learning Machine and Descartes Path Planning

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 97
Author(s):  
Suhua Li ◽  
Jiacheng Xie ◽  
Xuewen Wang ◽  
Fang Ren ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Extreme Learning Machine ◽  
Target Location ◽  
Industrial Robot ◽  
Prediction Method ◽  
Local Error ◽  
Hydraulic Support ◽  
Key Points ◽  
Scraper Conveyor ◽  
Learning Machine

As a floating system connecting hydraulic support and scraper conveyor, the path planning of pushing mechanism is of great significance for their coordinated movement. In this paper, a method for path planning of hydraulic support pushing mechanism based on extreme learning machine (ELM) and Descartes path planning is proposed. According to the motion characteristics of moving mechanism, it is transformed into industrial robot model, based on the characteristics of the coordinates of the key points on the ear seat of the scraper conveyor when advancing, a prediction method of the key points coordinates based on ELM is proposed, so the target location of the end-effector is obtained. The path of the joint is determined by polynomial path partition and Descartes path planning method. The path is modified by Gaussian filtering method, and the peak value of path obtained by planning is filtered out, and the path correction is realized. Finally, the virtual simulation test is carried out in Unity3D. The planned coordinate curve has Poisson-like distribution and approximately around the target coordinate curve, and local error and correction error are within 2 cm and 0.1 cm, respectively. The coordinate curve obtained by combining planning and correction has a better effect.

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