Dynamic velocity feed-forward compensation control with RBF-NN system identification for industrial robots

Song, Weike; Xiao, Juliang; Wang, Gang; Wu, Guodong

doi:10.1007/s12209-013-1870-6

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In the field of construction engineering, artificial neural networks are used to predict concrete strength and find the nonlinear input-output relationship between concrete strength and its influencing factors [9,10]. In addition, artificial neural networks are used in the field of plant diseases control [11][12][13], process control and optimization [14][15][16], troubleshooting [17][18][19], intelligent control of industrial product assembly line [20][21][22], robotic surgery [23][24][25], intelligent driving [26][27][28], chemical product development [29][30][31], signal processing [32][33][34], and so on.…”

Section: The Origin and Development Of Artificial Neural Networkmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

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Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

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