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.
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