A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks

Joseph P. Bellucci; Kenneth W. Bauer

首页> 外文期刊>International Journal of Quality Engineering and Technology >A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks

【24h】

A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks

机译：泰勒系列方法采用Kriging和径向基础函数神经网络的计算机模拟鲁棒参数设计方法

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

>Robust parameter design is used to identify a system's control settings that offer a compromise between obtaining desired mean responses and minimising the variability about those responses. Two popular combined-array strategies - the response surface model (RSM) approach and the emulator approach - are limited when applied to simulations. In the former case, the mean and variance models can be inadequate due to the high level of nonlinearity within many simulations. In the latter case, precise mean and variance approximations are developed at the expense of extensive Monte Carlo sampling. This paper extends the RSM approach to include nonlinear metamodels, namely kriging and radial basis function neural networks. The mean and variance of second-order Taylor series approximations of these metamodels are generated via the multivariate delta method and subsequent optimisation problems employing these approximations are solved. Results show that improved mean and variance prediction models, relative to the RSM approach, can be attained at a fraction of the emulator approach's cost.

机译：>鲁棒参数设计用于识别系统的控制设置，在获取所需的平均响应和最小化关于这些响应的可变性之间提供折衷。两个流行的组合阵列策略 - 响应面模型（RSM）方法和仿真器方法 - 当应用于模拟时受限。在前一个情况下，由于许多模拟中的高水平的非线性，平均值和方差模型可能不足。在后一种情况下，精确的平均值和方差近似是以广泛的蒙特卡罗采样的牺牲开发的。本文扩展了RSM方法来包括非线性元典，即Kriging和径向基函数神经网络。通过多元Δ方法产生二阶泰勒级近似的二阶泰勒级近似的平均值和方差，并解决了采用这些近似的随后的优化问题。结果表明，改进的平均值和方差预测模型相对于RSM方法可以在仿真器方法的成本的一小部分中获得。

著录项

来源
《International Journal of Quality Engineering and Technology》 |2017年第3期|共24页
作者
Joseph P. Bellucci; Kenneth W. Bauer;
展开▼
作者单位

Air Education and Training Command Studies and Analysis Squadron 100 H Street East Suite #3 JBSA-Randolph TX 78150 USA;

Department of Operational Sciences Air Force Institute of Technology 2950 Hobson Way Wright-Patterson AFB Ohio 45433 USA;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类质量管理;
关键词
robust parameter design; RPD; Taylor series; simulations; kriging; neural networks; radial basis function neural networks; RBFNN; delta method; dual response optimisation; metamodelling; response surface modelling; emulators;

机译：鲁棒参数设计;泰勒系列;模拟;克里格;神经网络;径向基函数神经网络;RBFNN;δ方法;双响应优化;元模型;响应面;响应面;

相似文献

外文文献
中文文献
专利

1. A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks [J] . Joseph P. Bellucci, Kenneth W. Bauer International Journal of Quality Engineering and Technology . 2017,第3期

机译：泰勒系列方法采用Kriging和径向基础函数神经网络的计算机模拟鲁棒参数设计方法
2. Noisy Time Series Prediction Using M-estimator Based Robust Radial Basis Function Neural Networks With Growing And Pruning Techniques [J] . Chien-Cheng Lee, Yu-Chun Chiang, Cheng-Yuan Shih, Expert systems with applications . 2009,第3p1期

机译：使用基于M估计器的鲁棒径向基函数神经网络的增长和修剪技术对噪声时间序列进行预测
3. Robust mode transition control of four-wheel-drive hybrid electric vehicles based on radial basis function neural network estimation-a simulation study [J] . Li Ling, Tao Fazhan, Fu Zhumu Compel . 2021,第4期

机译：基于径向基函数神经网络估计的四轮驱动混合动力电动车辆的鲁棒模式转换控制 - 模拟研究
4. E-tsRBF: Preliminary Results on the Simultaneous Determination of Time-Lags and Parameters of Radial Basis Function Neural Networks for Time Series Forecasting [C] . Parras-Gutierrez E., Rivas V., del Jesus M.J. Intelligent Systems Design and Applications, 2009. ISDA '09 . 2009

机译：E-tsRBF：同时确定径向基函数神经网络的时滞和参数的初步结果，用于时间序列预测
5. Adaptation of closed form regularization parameters with prior information to the radial basis function neural network for high frequency financial time series. [D] . Kajiji, Nina. 2001

机译：带有先验信息的闭式正则化参数适应于高频金融时间序列的径向基函数神经网络。
6. Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network [O] . Ye Wu, Xiaowen Sun 2021

机译：基于径向基函数（RBF）神经网络的企业管理资源调度优化与仿真
7. Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks [O] . Nobuyuki Kanazawa 2020

机译：径向基函数非线性时间序列分析的神经网络和供应冲击的时变效应
8. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks [R] . Burken, John J. 2005

机译：使用鲁棒伺服LQR和径向基函数神经网络的可重构飞行控制设计

A Taylor series approach to the robust parameter design of computer simulations using kriging and radial basis function neural networks

摘要

著录项

相似文献

相关主题

期刊订阅