Study of PSO Optimized BP Neural Network and Smith Predictor for MOCVD Temperature Control in 7 nm 5G Chip Process

Chang, Kuan‐Chang; Zhou, Yuwei; Wang, Hsiao-Chuan; Lin, Yuh-Chung; Chu, Kai-Chun; Hsu, Tsui-Lien; Pan, Jeng‐Shyang

doi:10.1007/978-3-030-58669-0_51

Cited by 6 publications

(1 citation statement)

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“…Song and Miao [20] implemented backpropagation neural network (BPNN) in damage prediction and risk assessment, and demonstrated the good effect of BPNN in predictive analysis and intelligent discrimination [21,22]. By improving the backpropagation algorithm, Chang et al [23][24][25] further enhanced the accuracy of BPNN in self-learning, prediction, and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of Coal Structure for the Control of Heat-Induced Gas Outburst and Energy-Efficient Mining

Li¹,

Yao²,

Wen³

et al. 2020

IJHT

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Coal structure could be roughly divided into four types. Among them, the two kinds of tectonic coal face a high risk of heat-induced gas outburst, which arises from the unfavorable temperature conditions in the coal structure. However, there is not yet an efficient way to identify the type of coal structure. The adjacent types of coal structures are often misjudged. The lack of an efficient identification method hinders the prevention of heat-induced gas outburst, making it difficult to realize energy-efficient and safe mining. To solve the problem, this paper first theoretically analyzes the ultrasonic properties of different types of coals, and applies backpropagation neural network (BPNN) to build up an intelligent identification model for the type of coal structure. Specifically, the characteristic parameters of ultrasonic signal were taken as the basis for judging the type of coal structure, the identification algorithm of BPNN was adopted to accurately identify the structure type of coal, and then the heat-induced gas outburst risk of the coal was evaluated preliminarily. Experimental results show that the proposed model could accurately identify the type of coal structure, and even differentiate between adjacent types of coals. The research results provide a reference for effective prevention of heat-induced gas outburst, and realization of energy-efficient and safe mining.

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