The PC-Algorithm of the Algebraic Bayesian Network Secondary Structure Training

Kharitonov, Nikita A.; Abramov, Maxim V.; Tulupyev, Alexander L.

doi:10.1007/978-3-030-86855-0_18

Cited by 1 publication

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“…This experiment used the Bayesian training algorithm [22], [23] to determine the feasibility of the fuzzy neural network system and the number of neurons in the hidden layer of the neural network system. The neural network system was trained using 800 groups of roadheader section error samples, with 10 to 15 neurons.…”

Section: ) Control System Feasibility Analysis and Simulationmentioning

confidence: 99%

Fuzzy Rule-Based Section Forming for Mining Robotic Excavation Error Compensation

Tian,

Liu,

et al. 2023

IEEE Access

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Based on the automatic forming technology for mine roadway sections and the error measurement technology for boom-type roadheaders, this study primarily uses a fuzzy neural network with a proportional-integral-derivative (PID) controller to compensate for pose errors. By modeling and analyzing the body position and section affecting the boundary accuracy of a boom-type roadheader, boundary compensation is performed according to the error between the preset and actual sections. The compensation accuracy is then analyzed, and the proposed automatic section-cutting control program is further modified to achieve a precise section formation of the comprehensive excavation face. The experimental results demonstrate that there is a decrease of 21 % and 28 % in the horizontal and vertical section errors, respectively; thus, the proposed program effectively reduces the roadway section errors and achieves precise cutting. This study promotes the automation, intelligence, and robotization of coal-mine roadway production and provides data and experimental support for the future development of intelligent boring machine technology.

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Section: ) Control System Feasibility Analysis and Simulationmentioning

confidence: 99%