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The Assessment of Intrinsically Safe Parameters Based on Artificial Intelligence

Author: ZhaoHong
Tutor: DingGuoPing;XuJianPing
School: Shanghai Jiaotong University
Course: Instrumentation Engineering
Keywords: Artificial Intelligence Electrical equipment explosion-proof Intrinsically Safe Parameters assessed Matlab
CLC: X913.4
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 0
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Abstract


This topic from the start with the theory of electrical explosion-proof , explosion-proof electrical equipment into the details of the significance of the modern industrial . Intrinsically safe technology occupies a crucial position in the electrical equipment in explosion-proof technology because of its advantages of product safety and high reliability , small size , light weight , can be electrically charged maintenance , manufacturing simple . Proof inspection agencies at home and abroad depends on the reference curve and spark test apparatus for intrinsically safe electrical equipment inspection and evaluation of major . In this paper, the basic principle of intrinsically safe explosion , analysis of the limitations of both traditional inspection methods to explore the impact of intrinsically safe electrical equipment evaluation on the spark ignited principle summarized the main factors affect the ability to spark ignition and nonlinearity between , uncertainty . Artificial Intelligence is a simulation of the human mind and the biological behavior of the science of emerging technologies to solve more complex problems . Artificial neural networks and genetic algorithms are two important areas of artificial intelligence , and both were to imitate the human brain physiological structure and biological evolution behavior , can be used to solve nonlinear optimization problems are difficult to find in traditional mathematical models . In this paper, spark ignition test , and collected a large number of test data ; application of multi-layer forward error back propagation neural network ( BP neural network ) technology to build intrinsically safe parameters assessment model , and discusses the important parameters of the BP network model screening set given ; introduction of genetic algorithms to the optimization of the initial value of the network constructed to overcome the BP network to place undue reliance on initial weights , thresholds, and easy to fall into local minimum points drawback ; Matlab neural network and genetic algorithm toolbox establish intrinsically safe The parameter assessment model . The last spark ignition test to verify the practicality of the model .

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CLC: > Environmental science, safety science > Safety Science > Safety Science and basic theory > Security Systems > Security systems engineering
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