基于相应簇回声状态网络静态分类方法

Echo State Networks for Static Classification with Corresponding Clusters

借鉴模仿哺乳动物大脑皮层分簇结构的复杂网络拓扑结构,提出一种基于相应簇储备池回声状态网络的分类方法.将时间窗函数机制引入到回声状态网络储备池的构建中,利用具体问题中需分类数据的类别数量,生成具有对应分簇数目的储备池,以期提高分类精度.基于标准数据集和模拟电路故障诊断的实验验证结果表明,本文方法与标准回声状态网络等方法相比具有更高的分类精度.

A classification method using echo state networks（ESNs） with corresponding clusters is proposed,which is inspired by complex network topologies imitating cortical networks of the mammalian brain.The time windows functions are adopted to construct multiple-cluster reservoir.The number of clusters corresponds with the number of classes in specific classification problems to improve the classification accuracy.Experimental results based on the standard datasets and analog circuit fault diagnosis show that the proposed method outperforms the original echo state networks.