OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Channel Estimation
Interference Cancellation
Stochastic Geometry
Energy Harvesting
Feedback
Bi-directional
Heterogeneous Networks
Equalization
HetNet
relay networks
FBMC
Ultra Low Power
SC-FDMA
TVWS
Duplex
Reliability
CDMA
MIMO
interference
channel capacity
in-band full-duplex system
interference suppression
5G
C-V2V
reinforcement learning
RSRP weighting
non-orthogonal multiple access (NOMA)
health care
5G mobile communication
indoor positioning
Vehicle-to-vehicle communication
estimated position overlapping
Resource sharing
Power allocation
multi-access edge computing
control overhead
hybrid
Rat-dependent positioning
NR positioning
smart factory
UFMC
Handoff
Femtocell
QAM
CoMP
power uncertainty
- Computation offloading
amplify and forward communication
Zigbee
body area networks
resource block management
frame structure
WVAN
inter user interference
GFDM
mode selection
antenna arrays
partial overlap
LTE-based V2V
resource selection
maximum likelihood method
Communication range
Number of training blocks
Vehicular communication
Uplink SCMA system
Dynamic TDD
QR Factorization
Metaheuristics
FS-NOMA
cross-link interference
user fairness
Multi-user Receiver
Mode 3
V2X
P-NOMA
dynamic HetNet
spectrum partitioning
DQN
D-TDD
CLI
massive connectivity
and 5G networks.
OTDOA
estimated position updating
distributed mode
non-orthogonal multiple access
Spatial capacity
LTE-TDD
—Device-to-device (D2D)
Location-based
overloading
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Sooyong Choi, Kyunbyoung Ko and Daesik Hong, "Adaptive Error-constrained Backpropagation Algorithm", IEEE NNSP, Sep. 2001
[Other Conf. Papers]
조회 79704
Status : | Presented |
---|---|
Date : | 2001-09 |
Title : | Adaptive Error-constrained Backpropagation Algorithm |
Authors : | Sooyong Choi, Kyunbyoung Ko and Daesik Hong |
Conference : | IEEE international workshop on Neural Networks for Signal Processing XI |
Abstract : | In order to accelerate the convergence speed of the conventional BP algorithm, constrained optimization techniques are applied to the BP algorithm. First, the noise-constrained least mean square algorithm and the zero noise-constrained LMS algorithm are applied (designated the NCBP and ZNCBP algorithms, respectively). These methods involve an important assumption: the filter or the receiver in the NCBP algorithm must know the noise variance. By means of extention and generalization of these algorithms, the authors derive an adaptive error-constrained BP algorithm and its simplified algorithm, in which the error variance is estimated. This is achieved by modifying the error function of the conventional BP algorithm using Lagrangian multipliers. The convergence speeds of the proposed algorithms are 20 to 30 times faster than those of the conventional BP algorithm, and are faster than or almost the same as that achieved with a conventional linear adaptive filter using an LMS algorithm |
URL : | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=943115 |
Download : | http://mirinae.yonsei.ac.kr/?module=file...7bfc71348f |
Sooyong Choi; KyunByoung Ko; Daesik Hong; , "Adaptive error-constrained
backpropagation algorithm," Neural Networks for Signal Processing XI, 2001.
Proceedings of the 2001 IEEE Signal Processing Society Workshop , vol., no.,
pp.103-112, 2001
doi: 10.1109/NNSP.2001.943115
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=943115&isnumber=20420
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