回声状态网络及其在图像边缘检测中的应用

Echo state networks and its application on image edge detection

循环神经网络(RNN,也称反馈神经网络)是一种重要的人工神经网络,与前馈神经网络相比具有更好的学习能力和更快的收敛速度,但其隐层结构的设计一直是个难点问题。回声状态网络(ESN)有效地解决了上述问题,相比于以前的循环神经网络,其具有结构独特、稳定性好、学习过程简单快捷等特点。介绍了回声状态网络及其学习方法,将其用于图像的边缘检测中,取得了良好的效果。

Recurrent Neural Networks （RNN） is a kind of important artificial neural networks with better ability for learning and rate of convergence in comparison with forward neural networks,however,the design of the structure of the hidden-layer is a difficult problem all the time.Echo State Networks has no such problems with special construction,good stability,short-cut learning process.Application of ESN to the edge detection of images has been introduced after the presentation of the structure and method of learning of ESN, resuhing well.