摘要
针对标准BP神经网络易陷入局部极小值的问题,本文结合全局随机搜索最优解的粒子群优化算法,建立了一种3D动漫造型评价模型,并将其应用到3D动漫造型的生成过程。该模型充分利用粒子群算法的全局寻优特性,优化BP网络的权值和阈值,使网络的均方误差小于或等于目标设定值。实验结果表明,本文方法在保证BP网络能收敛到全局最优解的前提下,加快了BP网络的收敛速度和收敛精度,并在3D动漫造型的进化中具有较好的评价性能,提高了造型的生成质量。
This paper constructs a 3D animation modeling evaluation model with Particle Swarm Optimization （PSO） algorithm and BP network in view of the issues of easy falling of standard BP neural network into local minimum and the global searching of PSO. We apply the model to the generation of 3D animation modeling. It fully utilizes the characteristic of global searching of PSO and optimizes the weights and thresholds of BP network, which makes mean-square error less than or equal to the preset value. Experimental results show that the approach improves the convergence rate and convergence precision of BP network based on the guarantee of the global optimization result. It has preferable evaluation capability in the evolution of 3D animation modelings and improves the quality of 3D animation modelings.
出处
《山东科学》
CAS
2012年第5期67-72,共6页
Shandong Science
基金
国家自然科学基金(60970004
60743010)
国家教育部博士点基金(20093704110002)
山东省自然科学基金(ZZ2008G02
ZR2010QL01)
关键词
微粒群算法
BP神经网络
全局寻优
动漫造型评价
particle swarm optimization
BP neural network
global optimization
3D animation modeling evaluation
