首页> 外文会议>International Symposium on Neural Networks >Immunity Clonal Synergetic Learning of Unbalanced Attention Parameters in Synergetic Network
【24h】

Immunity Clonal Synergetic Learning of Unbalanced Attention Parameters in Synergetic Network

机译:协同网络中不平衡注意参数的免疫克隆协同学习

获取原文

摘要

In this paper, we discuss the dynamical behavior of synergetic neural network and propose a new unbalanced attention parameters optimization algorithm based on Immunity Clonal algorithm (ICA). In comparison with the algorithms with balanced attention parameters and that with unbalanced attention parameters on GA, the new method has automatic balance ability between exploration and exploitation and is not easy to get into local optimum. In addition, iterative step is adjusted adaptively. Experiments on textural images and remote sensing images show that the presented algorithm has not only faster convergent rate but also better recognition rate.
机译:本文讨论了协同神经网络的动态行为,提出了一种基于免疫克隆算法(ICA)的新不平衡关注参数优化算法。与具有均衡注意参数的算法相比,在GA上具有不平衡的关注参数,新方法在勘探和开发之间具有自动平衡能力,并且不容易进入局部最佳。此外,自适应地调整迭代步骤。纹理图像和遥感图像的实验表明,所呈现的算法不仅具有更快的收敛速率,而且还具有更好的识别率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号