首页> 外文会议>Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on >Neuron identification by classification tree and particle swarm optimization
【24h】

Neuron identification by classification tree and particle swarm optimization

机译:通过分类树和粒子群算法进行神经元识别

获取原文

摘要

In this paper, the adaptive method, which is on the basis of hierarchical classification tree and particle swarm optimzation(PSO), is proposed to classify different neurons in the international benchmark samples. Firstly, the similarity, corresponding to the maximum and the minimum of each feature, is calculated to denote the characteristic of the neuron, and then this paper designs the rules of classification tree and utilizes PSO algorithm to optimizate crucial parameters in the classification tree. To show high performance and the effectiveness of this proposed algorithm, the successful percentage of discerning the neuron can achieve to 98.04%.
机译:本文提出了一种基于层次分类树和粒子群优化算法的自适应方法,对国际基准样本中的不同神经元进行分类。首先,通过计算每个特征的最大值和最小值对应的相似度来表示神经元的特征,然后设计分类树的规则,并利用PSO算法对分类树中的关键参数进行优化。为了显示该算法的高性能和有效性,识别神经元的成功百分比可以达到98.04%。

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