Skip to Content
Blind Equalization in Neural Networks
book

Blind Equalization in Neural Networks

by Liyi Zhang, Tsinghua University Tsinghua University Press
December 2017
Intermediate to advanced content levelIntermediate to advanced
268 pages
7h 59m
English
De Gruyter
Content preview from Blind Equalization in Neural Networks

Appendix A:Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN

In the complex feed-forward neural network (FFNN), the connection weight between the hidden layer and the input layer is wij(n), so the iterative formulas are

wij(n+1)=wij(n)μ2J(n)wij(n)(A.1)

J(n)wij(n)=2[ | x˜(n) |2R2 ]| x˜(n) |[ | x˜(n) |wij,R(n)+j| x˜(n) |wij,I(n) ](A.2)

According to eqs. (3.16)–(3.22), we obtain

| x˜(n) |wij,R(n)=x˜(n)x˜*(n)wij,R(n)=12| x˜(n) |[ x˜(n)x˜*(n) ]wij,R(n)=12| x˜(n) |{ f2[ vR(n) ]+f2[ vI(n) ] }wij,R

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Deep Learning for Chest Radiographs

Deep Learning for Chest Radiographs

Yashvi Chandola, Jitendra Virmani, H.S Bhadauria, Papendra Kumar
Nonlinear Filters

Nonlinear Filters

Peyman Setoodeh, Saeid Habibi, Simon Haykin
Adaptive Filtering

Adaptive Filtering

Alexander D. Poularikas

Publisher Resources

ISBN: 9783110449679