OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Stochastic Geometry
Bi-directional
Energy Harvesting
Heterogeneous Networks
FBMC
HetNet
Equalization
relay networks
Ultra Low Power
TVWS
MIMO
interference
SC-FDMA
CDMA
Duplex
channel capacity
interference suppression
5G
Reliability
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in-band full-duplex system
OCBT
CLI
body area networks
5G mobile communication
antenna arrays
NR positioning
health care
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LTE-TDD
FS-NOMA
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OTDOA
hybrid
control overhead
estimated position overlapping
resource block management
amplify and forward communication
power uncertainty
CoMP
quality of service
cellular radio
Rat-dependent positioning
telecommunication traffic
Handoff
Femtocell
inter user interference
UFMC
mode selection
GFDM
QAM
Zigbee
frame structure
Vehicular communication
Vehicle-to-vehicle communication
non-orthogonal multiple access
QR Factorization
Spatial capacity
LTE-based V2V
Number of training blocks
Communication range
user fairness
Mode 3
resource selection
distributed mode
maximum likelihood method
Metaheuristics
cross-link interference
Dynamic TDD
Uplink SCMA system
V2X
DQN
estimated position updating
—Device-to-device (D2D)
D-TDD
dynamic HetNet
indoor positioning
spectrum partitioning
- Computation offloading
multi-access edge computing
P-NOMA
partial overlap
Subband filtering
Multi-user Receiver
reinforcement learning
RSRP weighting
smart factory
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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 73587
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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