Neural Networks with Variable Activation Functions

›› 2001, Vol. 27 ›› Issue (12): 76-78.

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Neural Networks with Variable Activation Functions

LONG Xinhai, XIAO Tianyuan, CHEN Xiaofeng

(National CIMS Engineering Research Center, Department of Automation, Tsinghua Univ., Beijing100084)

Published:2001-12-25

采用变参数激励函数的人工神经网络

龙欣海,肖田元,陈晓峰

清华大学自动化系国家CIMS工程技术研究中心,清华大学自动化系国家CIMS工程技术研究中心,清华大学自动化系国家CIMS工程技术研究中心北京 100084

Abstract

Abstract: This paper presents a variable activation function to improve the non-linear capacity of neural network. Using genetic algorithms and MBP method to train the neural network can have the global convergence ability and high precision.

摘要： 隐层神经元采用相同的激励函数会限制网络的非线性表达能力,因此,提出变参数激励函数,在学习中同时调整网络权值和激励函数的参数,增强网络的表达能力。并使用遗传算法与MBP结合的学习算法训练网络。此方法具有全局收敛能力和很高的精度。

关键词: 人工神经网络, 激励函数, 修正BP算法, 遗传算法

CLC Number:

TP183

LONG Xinhai, XIAO Tianyuan, CHEN Xiaofeng. Neural Networks with Variable Activation Functions[J]. , 2001, 27(12): 76-78.

龙欣海,肖田元,陈晓峰. 采用变参数激励函数的人工神经网络[J]. , 2001, 27(12): 76-78.

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