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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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 78115
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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