A novel genetic-algorithm-based neural network for short-term load forecasting

被引：97

作者：

Ling, SH ^{[1
]}

Leung, FHF ^{[1
]}

Lam, HK ^{[1
]}

Lee, YS ^{[1
]}

Tam, PKS ^{[1
]}

机构：

[1] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2003年 / 50卷 / 04期

关键词：

genetic algorithm (GA); neural network; short-term load forecasting;

D O I：

10.1109/TIE.2003.814869

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.

引用

页码：793 / 799

页数：7

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