COMPARISON OF NEURAL-NETWORK MODELS FOR FAULT-DIAGNOSIS OF POWER-SYSTEMS

被引：19

作者：

RANAWEERA, DK

机构：

[1] Department of Electrical Engineering, Arizona State University, Tempe

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 1994年 / 29卷 / 02期

关键词：

NEURAL NETWORK MODELS; BACKPROPAGATION NETWORK; RADIAL BASIS FUNCTION NETWORK; POWER SYSTEM FAULT DIAGNOSIS;

D O I：

10.1016/0378-7796(94)90067-1

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 [电气工程]; 0809 [电子科学与技术];

摘要：

This paper compares the performance of two neural network (ANN) models for fault diagnosis of power systems. Radial basis function and back-propagation networks are compared with reference to generalization, training time and number of training patterns needed for each model.

引用

页码：99 / 104

页数：6

共 7 条

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[2]

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[3]

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[4]

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[5]

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[6]

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[7]

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