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Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

机译：基于遗传算法的神经网络函数输入选择近似器及其在ssmE健康监测中的应用

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A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

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Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.;
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年度 1991
页码 1-8
总页数 8
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
GENETIC ALGORITHMS; NEURAL NETS; PARAMETERIZATION; SPACE SHUTTLE MAIN ENGINE; SYSTEMS HEALTH MONITORING; FAULT DETECTION; MACHINE LEARNING;

机译：遗传算法;神经网络;参数化;空间穿梭机主要发动机;系统健康监测;故障检测;机器学习;

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专利

1. High-order neural network structure selection for function approximation applications using genetic algorithms [J] . Rovithakis G.A., Chalkiadakis I., Zervakis M.E. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics . 2004,第1期

机译：应用遗传算法进行函数逼近的高阶神经网络结构选择
2. LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network [J] . Jingqiu Ren, Ke Bao, Guanghua Zhang, International Journal of Distributed Sensor Networks . 2020,第2期

机译：基于噪声遗传算法 - 径向基础函数神经网络的基于密度为基于密度的空间聚类的Landmarc室内定位算法
3. Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection [J] . Vanina G.Franco, Juan C.Perin, Victor E.Mantovani, Talanta: The International Journal of Pure and Applied Analytical Chemistry . 2006,第3期

机译：FTIR光谱结合人工神经网络监测生物过程中的底物和产品，通过基于遗传算法的波长选择方法进行增强
4. Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring [C] . Peck, C.C., Dhawan, . 1993

机译：基于遗传算法的神经网络函数逼近器输入选择及其在SSME健康监测中的应用
5. Hybrid genetic algorithms and neural network based function approximators for water quality modeling. [D] . Huang, Yongtai. 2009

机译：混合遗传算法和基于神经网络的函数逼近器，用于水质建模。
6. The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering [O] . Jie Luo, Ran Ren, Kangde Guo, 2020

机译：后传播神经网络与遗传算法基础坑的变形监测及其在岩土工程中的应用
7. Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring [O] . Peck, Charles C., Meyer, Claudia M., Dhawan, Atam P. 1991

机译：基于遗传算法的神经网络函数逼近器的输入选择及其在SSME健康监测中的应用

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

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