Beyond weights adaptation: a new neuron model with trainable activation function and its supervised learning

Author

Wu, Youshou ; Zhao, Mingsheng ; Ding, Xiaoqing

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing, China

Volume

2

fYear

1997

fDate

9-12 Jun 1997

Firstpage

1152

Abstract

This paper proposes a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived by training a primitive neuron activation function. BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Two simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN

Keywords

backpropagation; feedforward neural nets; multilayer perceptrons; BP-like learning algorithm; M-P model; MFNN; TAF; backpropagation; multilayer feedforward neural net; neuron model; primitive neuron activation function training; supervised learning; trainable activation function; weight adaptation; Artificial neural networks; Biological neural networks; Extraterrestrial measurements; Image processing; Nervous system; Neural networks; Neurons; Performance analysis; Research and development; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.616194

Filename

616194