摘要
为解决运用Hopfield神经网络优化算法处理图像分割存在的收敛速度与局部最优的矛盾,采用模拟退火策略与遗传算法结合的优化方法来改进传统的优化算法,对迭代收敛后的Hopfield网络在局部范围内运用模拟退火遗传算法,以搜索阈值平面全局最优解,进行图像分割。实验证明,采用此方法可以得到较好的分割效果。
A combined optimization of genetic algorithms with simulated annealing has been applied to image segmentation with good results in this paper.The defect of Hopfield neural network is being captured by local optimal solutions,while the defect of genetic algorithms is the low speed of convergence.Both disadvantages mentioned above have been overcome here.Solutions obtained with the converged Hopfield neural network are applied to the genetic algorithm to search for the optimization on the plane of threshold value.
出处
《武汉理工大学学报》
CAS
CSCD
北大核心
2011年第5期161-164,共4页
Journal of Wuhan University of Technology
基金
中央高校基本科研业务费专项资金(2010-ZX-010)
关键词
HOPFIELD网络
模拟退火
遗传算法
优化
Hopfield neural network
simulated annealing
genetic algorithms
optimization