摘要
利用神经网络方法解决逆向工程设计中的自由曲面重建问题，在ＢａｃｋＰｒｏｐａｇａｔｉｏｎ（简称ＢＰ）算法的基础上，吸取了模拟退火算法的优点，神经网络的学习采用按概率随机接受一个不成功训练值的方法，使学习跳出局部最优解，最终收敛于全局最优解．试验证明，用这种方法解决自由曲面的重建问题，可以达到良好的效果，优于以往文献提出的方法．
This article presents an artificial neural network approach to solve the problem of reconstruction and manufacture of freeform surfaces in reverse engineering. Taking advantage of the global minimum property of Simulated Annealing Procedure, a technique is proposed to accept a temporally failed training result in accordance to probability. Using this technique, the training can jump out of the local minimum and converge to the global minimum. The method is better than the algorithm given in the article , when it is used to solve the problem of reconstruction and manufacture of freeform surfaces.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
1998年第3期193-199,共7页
Journal of Computer-Aided Design & Computer Graphics
关键词
自由曲面
神经网络
BP算法
模拟退火
CAD
CAM
reverse engineering, freeform surfaces, neural networks, BP algorithm, simulated annealing
