OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Energy Harvesting
Stochastic Geometry
Bi-directional
Equalization
relay networks
FBMC
HetNet
CDMA
channel capacity
SC-FDMA
interference
MIMO
Ultra Low Power
Duplex
TVWS
CLI
in-band full-duplex system
D-TDD
interference suppression
Reliability
5G
C-V2V
indoor positioning
reinforcement learning
RSRP weighting
User grouping
Rat-dependent positioning
Communication range
multi-access edge computing
hybrid
- Computation offloading
—Device-to-device (D2D)
estimated position updating
control overhead
resource block management
estimated position overlapping
Femtocell
NR positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
packet delay
power uncertainty
ultra-dense small cell network
GFDM
antenna arrays
5G mobile communication
UFMC
WVAN
mode selection
inter user interference
health care
Vehicular communication
cross-link interference
resource selection
FS-NOMA
Dynamic TDD
Number of training blocks
Mode 3
Uplink SCMA system
Multi-user Receiver
LTE-TDD
user fairness
smart factory
V2X
Metaheuristics
Location-based
QR Factorization
partial overlap
P-NOMA
dynamic HetNet
spectrum partitioning
Cell-free
and 5G networks.
overloading
massive connectivity
DQN
mMIMO
distributed mode
non-orthogonal multiple access
OTDOA
maximum likelihood method
Resource sharing
Power allocation
non-orthogonal multiple access (NOMA)
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Sooyong Choi, Kyunbyoung Ko and Daesik Hong, "Adaptive Error-constrained Backpropagation Algorithm", IEEE NNSP, Sep. 2001
[Other Conf. Papers]
조회 84922
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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