An embedded probabilistic neural network with on-chip learning capability

Author

Jen-Huo Wang ; Kea-Tiong Tang ; Hsin Chen

Author_Institution

Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan

fYear

2013

fDate

Oct. 31 2013-Nov. 2 2013

Firstpage

29

Lastpage

32

Abstract

An embedded system capable of recognizing biomedical signals reliably is important for fusing sensory data of portable or implantable microsystems in biomedical applications. This paper presents the digital VLSI implementation of the probabilistic neural network, called the Continuous Restricted Boltzmann Machine (CRBM), which is able to cluster or to classify sensory data of an electronic nose. The learning algorithm of the CRBM is also realized on the same chip, such that the CRBM system is able to optimize its parameters automatically, or to compensate for sensory drifts by on-line learning.

Keywords

Boltzmann machines; VLSI; biomedical electronics; electronic noses; lab-on-a-chip; learning (artificial intelligence); probability; CRBM learning algorithm; biomedical signal recognition; continuous restricted Boltzmann machine; digital VLSI implementation; electronic nose; embedded probabilistic neural network; embedded system; implantable microsystems; on-chip learning capability; on-line learning; parameter optimization; portable microsystems; sensory data classification; sensory data fusion; Approximation methods; Neural networks; Neurons; Noise; Probabilistic logic; Training; Very large scale integration; CRBM; on-line learning; probabilistic neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE

Conference_Location

Rotterdam

Type

conf

DOI

10.1109/BioCAS.2013.6679632

Filename

6679632