Justification of a neuron-adaptive activation function

被引：7

作者：

Xu, SX ^{[1
]}

Zhang, M ^{[1
]}

机构：

[1] Univ Tasmania, Sch Comp, Launceston, Tas 7250, Australia

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL III | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.861351

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An empirical justification of a neuron-adaptive activation function for feed-forward neural networks has been proposed in this paper. Simulation results reveal that feed-forward neural networks with the proposed neuron-adaptive activation function present several advantages over traditional neuron-fixed feed-forward networks such as increased flexibility, much reduced network size, faster learning, and lessened approximation errors.

引用

页码：465 / 470

页数：6

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