.png){kind=icon}
.png){kind=icon}
Return
Bayesian regularization neural networks for optimizing fluid flow processes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cma.2005.01.015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper details the application of neural networks and evolutionary strategies for shape optimization problems. These, commonly grouped under the name soft computing methods, are quite recent methods and in demand for application to engineering optimization tasks. For complex problems, like in non-convex optimization, such heuristic techniques are able to outperform conventional optimization methods. We show that the use of progressive network models can yield satisfactory results when optimizing engineering relevant applications. Numerical examples are given. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
shape optimization
neural networks
Bayesian regularization
evolutionary algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
1.3W
Citations:
5.6W
Organization
No organization information available