回复式离散神经网络的特征子空间估值(英文)

Eigensubspace Estimation Using Discrete Recurrent Neural Networks

提出了用两种回复式离散神经网络模型研究正定对称矩阵的特征子空间估值问题:第1种模型是非线性神经网络,用于计算最大特征值及其特征向量;第2种模型属于线性神经网络,用于计算相应于最大特征值的特征子空间。详细研究了两种离散神经回路网络模型的动力学性质并用于特征子空间估值。

This paper proposes two models of discrete recurrent neural networks to study the problem of eigensubspace estimation for positive definite symmetric matrix. The first model is a class of nonlinear neural networks. It is used for estimating the largest eigenvalue and one of its corresponding eigenvectors. The second model is a class of linear neural networks which estimates the eigensubspace corresponding to the largest eigenvalue. Dynamic properties of these two classes of discrete recurrent neural network models are studied and used for eigensubspace estimation.