...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal Information Theories and Applications >A New Approach for Eliminating the Spurious States in Recurrent Neural Networks
【24h】

A New Approach for Eliminating the Spurious States in Recurrent Neural Networks

机译：在递归神经网络中消除杂散状态的新方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As is well known, the Convergence Theorem for the Recurrent Neural Networks, is based in Lyapunov′s second method, which states that associated to any one given net state, there always exist a real number, in other words an element of the one dimensional Euclidean Space R , in such a way that when the state of the net changes then its associated real number decreases. In this paper we will introduce the two dimensional Euclidean space R 2 , as the space associated to the net, and we will define a pair of real numbers ( ) , x y , associated to any one given state of the net. We will prove that when the net change its state, then the product x y . will decrease. All the states whose projection over the energy field are placed on the same hyperbolic surface, will be considered as points with the same energy level. On the other hand we will prove that if the states are classified attended to their distances to the zero vector, only one pattern in each one of the different classes may be at the same energy level. The retrieving procedure is analyzed trough the projection of the states on that plane. The geometrical properties of the synaptic matrix W may be used for classifying the n-dimensional state- vector space in n classes. A pattern to be recognized is seen as a point belonging to one of these classes, and depending on the class the pattern to be retrieved belongs, different weight parameters are used. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presented
机译：众所周知，递归神经网络的收敛定理基于李雅普诺夫的第二种方法，该方法表明与任何给定的净状态相关的总存在实数，换句话说就是一维元素欧几里得空间R，使得当网络的状态改变时，其关联的实数减少。在本文中，我们将引入二维欧式空间R 2，作为与网络关联的空间，并且将定义一对与网络的任何给定状态关联的实数（）x y。我们将证明，当网络改变其状态时，乘积x y。将减少。所有在能量场上的投影都位于同一双曲表面上的状态将被视为具有相同能级的点。另一方面，我们将证明，如果按照状态到零向量的距离对状态进行分类，则每个不同类别中的每个模式只能处于相同的能级。通过状态在该平面上的投影来分析检索过程。突触矩阵W的几何特性可以用于将n维状态向量空间分类为n类。要识别的模式被视为属于这些类别之一的点，并且根据要检索的模式所属的类别，使用不同的权重参数。网络的容量得以提高，杂散状态得以降低。为了阐明和证实理论结果，结合形式理论，提出了一种应用

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