Next Article in Journal
An Efficient Wireless Recharging Mechanism for Achieving Perpetual Lifetime of Wireless Sensor Networks
Previous Article in Journal
A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(1), 16; doi:10.3390/s17010016

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

1
Division of Electronics Engineering, Intelligent Robot Research Center, Chonbuk National University, Jeonbuk 54896, Korea
2
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wendong Xiao
Received: 29 September 2016 / Revised: 9 December 2016 / Accepted: 19 December 2016 / Published: 23 December 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [5017 KB, uploaded 23 December 2016]   |  

Abstract

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. View Full-Text
Keywords: software-based learning; circuit-based learning; complementary learning; backpropagation; RWC software-based learning; circuit-based learning; complementary learning; backpropagation; RWC
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, C.; Kim, H.; Adhikari, S.P.; Chua, L.O. A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms. Sensors 2017, 17, 16.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top