• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Efficient NURBS surface fitting via GA with SBX for free-form representation
【24h】

Efficient NURBS surface fitting via GA with SBX for free-form representation

机译：高效的NURBS表面拟合通过GA与SBX进行自由形式表示

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An accurate technique to perform NURBS surface fitting via genetic algorithms is presented. In this technique, the initial NURBS surface is generated by using object points as control points. Then, the genetic algorithm computes the weights and control points to obtain the NURBS surface fitting. The genetic algorithm is implemented through an objective function, which is deduced from NURBS surface and object points. The objective function is minimised by means of simulated binary crossover. This procedure is carried out based on the initial NURBS surface and NURBS surface constructed by employing the object height average as control point. Thus, the genetic algorithm provides the weights and control points of the NURBS surface that represent the object shape. The proposed algorithm improves the accuracy and speed of the NURBS fitting, which is created via genetic algorithms and gradient methods. It is because the proposed algorithm calculates the weights and control points from a known search space, which is produced by NURBS surfaces. Thus, the genetic algorithm minimises the objective function in fast form with high accuracy. The contribution of the proposed method is corroborated by an evaluation based on accuracy and speed of the traditional genetic algorithms and gradient methods.
机译：提出了一种通过遗传算法执行NURBS表面拟合的准确技术。在该技术中，通过使用对象点作为控制点来生成初始NURBS表面。然后，遗传算法计算权重和控制点以获得NURBS表面配件。遗传算法通过目标函数实现，该函数被从NURBS表面和对象点推导出来。通过模拟二进制交叉缩短目标函数。该过程基于通过使用物体高度平均作为控制点来构造的初始NURBS表面和NURBS表面来执行。因此，遗传算法提供了代表物形状的NURBS表面的权重和控制点。该算法提高了NURBS拟合的准确性和速度，通过遗传算法和梯度方法创建。这是因为所提出的算法计算来自已知搜索空间的权重和控制点，由NurBS表面产生。因此，遗传算法以高精度以快速形式最小化目标函数。基于传统遗传算法和梯度方法的准确度和速度，通过评估来证实所提出的方法的贡献。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号