位置：成果数据库 > 期刊 > 期刊详情页
A prior-knowledge input LSSVR metamodeling method with tuning based on cellular particle swarm optim
  • ISSN号：0957-4174
  • 期刊名称：Expert Systems with Applications
  • 时间：2014.4
  • 页码：2111-2125
  • 相关项目：基于目标级联分析和变可信度近似的复杂机械产品多学科设计优化研究
作者： Shao, Xinyu|Gao, Liang|Jiang, Ping|Qiu, Haobo|
同期刊论文项目
基于目标级联分析和变可信度近似的复杂机械产品多学科设计优化研究
期刊论文 28 会议论文 5
同项目期刊论文
A local adaptive sampling method for reliability-based design optimization using Kriging model
An ImportanceBoundary Sampling Method for Reliability-Based Design Optimization Using Kriging Model
An adaptive decoupling approach for reliability-based design optimization
An optimal shifting vector approach for efficient probabilistic design
An Importance Boundary Sampling Method for Reliability-Based Design Optimization Using Kriging Model
Anenhanced RBF-HDMR integrated with an adaptive sampling method for approximatinghigh dimensional pr
A local sampling method with variable radius for RBDO using Kriging
An adaptive SVR-HDMR model for approximating high dimensional problems
Difference mapping method using least square support vector regression for variable-fidelity metamod
Variable fidelity metamodel-based analytical target cascading method for green design
A hybrid variable-fidelity global approximation modelling method combining tuned radial basis functi
A local Kriging approximation method using MPP for reliability-based designoptimization
Analytical targetcascading using ensemble of surrogates for engineering design problems
Multi-stage design space reduction and metamodeling optimization method based on self-organizing map
Engineering Design Based on Hammersley Sequences Sampling Method and SVR
A Reliability Index Based Decoupling Method for Reliability-Based Design Optimization
A design space exploration method using Artificial Neural Networks and metamodeling
Variable-fidelity multidisciplinary design optimization based on analytical target cascading framewo
Reliability Analysis Method based on Surpport Vector Machines Classification and Adaptive Sampling S