Paper Info
Title
Optimal Design Of Both Rectified Layer And Pooling Layer Of Convolutional Neural Network For Noninvasive Blood Glucose Estimation System
Abstract
This paper proposes the optimal designs of both the rectified layer and the pooling layer of the convolutional neural network for a non-invasive blood glucose estimation system. The activation function of the neuron in the rectified layer is modelled by a high dimensional Gaussian function. The optimal design of the rectified layer becomes the optimal design of the parameters in the high dimensional Gaussian function. On the other hand, the pooling layer of the convolutional neural network is to represent a certain number of the outputs of the rectified layer by a value. In this paper, this representation value is defined as the Lp norm of a certain number of the outputs of the rectified layer, and the value of p is found via finding the solution of a smooth optimization problem. By finding the solutions of these optimization problems, the designed convolutional neural network is used in a non-invasive system for estimating the blood glucose concentration.
Year
Venue
Keywords
2016
2016 IEEE 14TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN)
convolutional neural network, blood glucose estimation, optimization
Field
DocType
ISSN
Mathematical optimization,Activation function,Convolution,Convolutional neural network,Computer science,Pooling,Lp space,Algorithm,Optimal design,Real-time computing,Gaussian function,Optimization problem
Conference
1935-4576
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xin Wu100.34
Yuwei Liu200.34
Jing Su300.68
Ya Li400.68
Wing-Kuen Ling594.21
Chi-Kong Li601.01