• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Computational Intelligence and Neuroscience >Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA
【2h】

Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

机译：基于PLS和CA的Elman神经网络优化分类算法研究

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion.
机译：特征变量之间以及样本之间的高维大型样本数据集可能会导致一些相关或重复性因素，占用大量存储空间，并消耗大量计算时间。使用Elman神经网络进行处理，输入过多会影响操作效率和识别精度；过多的同时训练样本，以及无法获得精确的神经网络模型，也限制了识别的准确性。针对这些系列问题，我们将偏最小二乘（PLS）和聚类分析（CA）引入到Elman神经网络算法中，通过PLS进行降维，可以消除特征的相关和重复因素。使用CA消除了样本的相关和重复因素。如果某个子类成为具有高维特征和较少数量的小样本，PLS将显示出独特的优势。每个子类都被视为一个训练样本，用于训练不同的精确神经网络模型。然后，使用相应的神经网络对其进行识别，将模拟样本进行区分并划分为不同的子类。建立了基于PLS和CA的优化Elman神经网络分类算法（PLS-CA-Elman算法）。新算法旨在提高操作效率和识别精度。通过实例分析，新算法具有独特的优越性，值得进一步推广。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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