摘要
针对采用反向传播算法的神经网络(Backpropagation:BP)作为实现仿生模式识别理论的工具时所遇到的最佳结构设计问题,提出了一个基于提高仿生模式识别系统性能的BP神经网络结构设计准则。该准则基于无论是在不同类样本点间还是同类样本点间都存在有先验知识这一结论,通过利用同类样本中所蕴含的先验知识对神经网络的结构进行设计,不仅能够确保仿生模式识别过程中的学习过程有效收敛,而且也使得网络结构的设计满足特定任务的要求。交通标识符识别的对比实验结果证明了该设计准则的有效性。
A novel theoretical framework of BP is proposed to solve the problem of designing a optimal networks structure when feedforward neural networks are used for biomimetic pattern recognition. The main view of this framework is that some prior knowledge consists in both different samples and congener samples. When the prior knowledge is used to design networks structure, it can not only ensure BP network to avoid the local minima of error surface in the network learning, but also meet some special needs in application of biomimetic pattern recognition. The recognition experiment of traffic ID demonstrates that this framework is a more effective method than the normal BP networks framework.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2007年第4期605-608,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60375011)
安徽省优秀青年科技基金(04042044)
教育部"新世纪优秀人才计划"项目资助课题
关键词
仿生模式识别
神经网络结构
同类样本覆盖
设计规范
biomimetic pattern recognition
neural networks structure
congener sample covering
design criterion
