OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Bi-directional
Energy Harvesting
Stochastic Geometry
HetNet
relay networks
FBMC
Equalization
channel capacity
TVWS
CDMA
interference
in-band full-duplex system
Duplex
MIMO
Ultra Low Power
C-V2V
5G
Reliability
SC-FDMA
interference suppression
D-TDD
CLI
indoor positioning
reinforcement learning
RSRP weighting
- Computation offloading
smart factory
Cell-free
multi-access edge computing
estimated position overlapping
—Device-to-device (D2D)
estimated position updating
mMIMO
control overhead
hybrid
NR positioning
Femtocell
Rat-dependent positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
User grouping
power uncertainty
ultra-dense small cell network
mode selection
antenna arrays
5G mobile communication
UFMC
resource block management
inter user interference
WVAN
health care
partial overlap
GFDM
Dynamic TDD
Multi-user Receiver
Number of training blocks
Uplink SCMA system
V2X
Vehicular communication
cross-link interference
LTE-TDD
FS-NOMA
Location-based
user fairness
Mode 3
QR Factorization
Metaheuristics
P-NOMA
non-orthogonal multiple access
dynamic HetNet
spectrum partitioning
and 5G networks.
massive connectivity
non-orthogonal multiple access (NOMA)
overloading
DQN
OTDOA
distributed mode
Communication range
resource selection
maximum likelihood method
Resource sharing
Power allocation
packet delay
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Sooyong Choi, Kyunbyoung Ko and Daesik Hong, "Adaptive Error-constrained Backpropagation Algorithm", IEEE NNSP, Sep. 2001
[Other Conf. Papers]
조회 82554
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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