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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Deasik Hong and O. K. Ersoy, "Parallel, Self-Organizing Hierarchical Neural Networks for vision and systems control", IEEE IMC, Aug. 1990
[Other Conf. Papers]
조회 81077
Status : | Presented |
---|---|
Date : | 1990-08 |
Title : | Parallel, Self-Organizing Hierarchical Neural Networks for vision and systems control |
Authors : | Deasik Hong and O. K. Ersoy |
Conference : | Proceedings of the IEEE International Workshop on Intelligent Motion Control |
Abstract : | A new neural network architecture called the parallel self-organizing hierarchical neural network (PSHNN) is presented. This new architecture involes a number of stages in which each stage can be a particular neural network (SNN). At the end of each stage, errordetection is carried out, and a number of input vectors are rejected. Between two stages there is a nonlinear transformation of those input vectors rejected by the previous stage. The new architecture has many desirable properties such as optimized system complexity in the sense of minimized self-organizing number of stages, high classification accuracy, minimized learning and recall times, and truly parallel architectures in which all stages are operating simultaneously without waiting for data from each other during testing. The experiments performed in comparison to multilayered networks with backpropagation training indicated the superiority of the new architecture. |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...ber=687316 |
Ersoy, O.K.; Hong, D.; , "Parallel, Self-Organizing, Hierarchical Neural Networks for Vision and Systems Control," Intelligent Motion Control, 1990. Proceedings of the IEEE International Workshop on , vol.1, no., pp.193-198, 20-22 Aug 1990
doi: 10.1109/IMC.1990.687316URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=687316&isnumber=14796
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