Determination of neural-network topology for partial discharge pulse pattern recognition

被引：67

作者：

Salama, MMA ^{[1
]}

Bartnikas, R

机构：

[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada

[2] Inst Rech Hydro Quebec, Varennes, PQ J3X 1S1, Canada

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 02期

关键词：

cascaded output; cavity size classification; network topology; partial discharges; time series approach;

D O I：

10.1109/72.991430

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A time-series approach has been employed to devise neural-network topologies for time dependent partial discharge pulse pattern recognition applications. The cascaded output neural-network structure was found to provide the highest success rate in differentiating between two different partial discharge patterns. This was accomplished by utilizing the indexed feature of the first stage output as one of the inputs into the second stage of the cascaded neural network.

引用

页码：446 / 456

页数：11

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