Research on Characteristics of Nonlinear Analog Devices Based on RBF Neural Network

DOI

10.2991/icemc-17.2017.6How to use a DOI?

Keywords

Artificial Neural Network; Radial Basis Function; Tunnel Diodes; Volt-ampere Characteristics

Abstract

Radial basis function (RBF) neural network is a novel and effective feedforward neural network which has been widely used in nonlinear time series prediction. In this paper, the tunnel diode is selected as a modeling object to measure the input and output characteristics in the laboratory environment. We analyze the necessity and feasibility of this modeling method in the actual circuit design, and can use the EDA tool to design the component model independently, and give us a more valuable circuit simulation before the analog circuit design.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liu Yiwei
PY  - 2016/06
DA  - 2016/06
TI  - Research on Characteristics of Nonlinear Analog Devices Based on RBF Neural Network
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)
PB  - Atlantis Press
SP  - 21
EP  - 27
SN  - 2352-538X
UR  - https://doi.org/10.2991/icemc-17.2017.6
DO  - 10.2991/icemc-17.2017.6
ID  - Yiwei2016/06
ER  -