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 for vision and systems control", IEEE IMC, Aug. 1990
[Other Conf. Papers]
조회 83090
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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