势能记忆梯度优化的协同模式分类方法  

Synergetic pattern recognition training algorithm based on potential energy function memory gradient optimized

作　　者：倪晓军[1] 李培根[1] 邹刚[1] 

机构地区：[1]国防科技大学信息中心,长沙410073

出　　处：《计算机工程与应用》2011年第36期211-214,共4页Computer Engineering and Applications

摘　　要：传统的协同模式分类学习方法是依据原型向量再通过伪逆或M-P广义逆的方法求出满足一定关系式的伴随向量,当样本维数大时,这种方法学习过程较慢,特别当样本维数有变化时传统的方法就不太适用了;协同势能函数优化的方法是直接利用协同动力学过程,来获得原型向量和伴随向量的收敛值,相比于传统的方法具有一定的优势。将最优化理论引入到协同进化的动力学过程,以加快学习过程的收敛,并以记忆梯度法替代了传统的梯度下降的算法进行势能函数的优化,来同时进行原型向量和伴随向量的学习,新方法能显著地提高收敛速度并获得较优的原型向量。通过图像的分类识别表明,相对于传统的方法,能提高识别率且收敛更好。The traditional training method of synergetic pattern recognition is figured out adjoint vector from prototype vector according to certain rules for example pseudo-inverse or M-P generalized-inverse.The course is slowly to the high-dimension samples,and is not fit to the change of dimension.The studying of potential energy function dynamics process can train prototype vector and adjoint vector meanwhile.The optimization approach is introduced to synergetic dynamics evolution process, using the memory gradient algorithm instead of the steepest decent algorithm to get optimization prototype vector.The prototype vector experimental result on images shows that the new algorithm can effectively search the prototype vector and ad- joint vector meanwhile,and excellent,correct and fast recognition result shows the new algorithm is more available than traditional training method.

关 键 词：协同模式识别 最优化方法 协同势能函数 记忆梯度法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]