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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Deasik Hong and O. K. Ersoy, "Parallel, Self-Organizing Hierarchical Neural Networks-Ⅱ", IEEE HICSS, Jan. 1990
[Other Conf. Papers]
조회 86031
Status : | Presented |
---|---|
Date : | 1990-01 |
Title : | Parallel, Self-Organizing Hierarchical Neural Networks-Ⅱ |
Authors : | Deasik Hong and O. K. Ersoy |
Conference : | Proceedings of Hawaii International Conference on System Sciences (HICSS) |
Abstract : | A neural network architecture called the parallel self-organizing hierarchical neural network (PSHNN) is discussed. The PSHNN involves a number of stages in which each stage can be a particular neural network (SNN). At the end of each SNN, error detection is carried out, and a number of input vectors are rejected. Between two SNNs there is a nonlinear first SNN. The PSHNN has an optimized system complexity in the sense of minimized self-organizing number of stages, high classification accuracy, minimized learning and recall times, and parallel architectures in which all SNNs operate simultaneously without waiting for data from each other during testing. In classification experiments with aircraft and satellite remote-sensing data the PSHNN is compared to multilayer networks using backpropagation training |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...ber=205112 |
Download : | http://mirinae.yonsei.ac.kr/?module=file...433f3d5b63 |
Ersoy, O.K.; Hong, D.; , "Parallel, self-organizing hierarchical neural networks," System Sciences, 1990., Proceedings of the Twenty-Third Annual Hawaii International Conference on , vol.i, no., pp.158-169 vol.1, 2-5 Jan 1990
doi: 10.1109/HICSS.1990.205112
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=205112&isnumber=5261
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