OFDM
Multiple Antennas
Cognitive Radio
Relay
CDMA
Synchronization
Channel Estimation
Spectrum Sharing
Interference Cancellation
Full duplex
Spectrum Sensing
Resource Allocation
Neural Networks
Stochastic Geometry
Equalizer
Bi-Directional
Feedback
Femtocell
Energy Harvesting
Heterogeneous Networks
Device-to-Device (D2D)
Cross-link interference
Cell Search
SINR mismatch problem
Ultra-dense small cell networks
HetNet
Idle cells
Spectral efficiency
Dynamic TDD
interference management
NOMA
FBMC
selection diversity
Handoff
outage probability
mode selection
B5G
multi-spectral
achievable sum rate
Preamble
Filtered OFDM
tabu-search
bursty traffic model
5G
flexible duplex
Grant-free Transmission
interference mitigation
Asynchronous Transmission
Shortened TTI
automatic repeat request (ARQ)
Two-way communications
transmission capacity (TC)
in-band full-duplex system
self-interference cancellation
Complexity
Ultra-dense small cell
sensing duration
Correlated MIMO
Cognitive relay networks
Heterogeneous channel estimation capability
Bi-directional full-duplex
full-duplex cellular
full-duplex relay
Simultaneous Sensing and Transmission
UWB
OQAM
Windowing
Vehicle-to-vehicle communication
prototype filter
pilot signal
LTE-based V2V
link reliability
resource size control
interference to noise ratio
eigen decomposition
TS-W-OFDM
C-V2V
massive connectivity
Reliability
Vehicle-to-Vehicle
full-spreading NOMA
Resource management
Cooperative systems
Coexistence scenarios
mixed numerology
6G
Cell-free
Coexisting network
Multiple access
MLP
CP-OFDM
Deep learning
Edge computing
Full-duplex
Railway
non-orthogonal multiple access
Mobility
HST
Computation offloading
TDD configuration
Singular Vale Decomposition
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Daesik Hong and Okan K. Ersoy, "Parallel, Self-Organizing Hierarchical Neural Networks-II", IEEE Trans. Industrial Elec., April 1993
[IEEE Trans. Ind. Electron.]
조회 31811
Status : | Published |
---|---|
Date : | 1993-04 |
Title : | Parallel, Self-Organizing Hierarchical Neural Networks-II |
Authors : | Daesik Hong and Okan K. Ersoy |
Journal : | IEEE TRANSACTION ON INDUSTRIAL ELECTRONICS |
Abstract : | Parallel, self-organizing, hierarchical neural networks (PSHNNs) involve a number of stages with error detection at the end of each stage, i.e., rejection of error-causing vectors, which are then fed into the next stage after a nonlinear transformation. The stages operate in parallel during testing. Statistical properties and the mechanisms of vector rejection of the PSHNN are discussed in comparison to the maximum likelihood method and the backpropagation network. The PSHNN is highly fault tolerant and robust against errors in the weight values due to the adjustment of the error detection bounds to compensate errors in the weight values. These properties are exploited to develop architectures for programmable implementations in which the programmable parts are reduced to on-off or bipolar switching operations for bulk computations and attenuators for pointwise operations |
URL : | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=222643 |
Ersoy, O.K.; Hong, D.; , "Parallel, self-organizing, hierarchical neural networks. II," Industrial Electronics, IEEE Transactions on , vol.40, no.2, pp.218-227, Apr 1993
doi: 10.1109/41.222643
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=222643&isnumber=5803
doi: 10.1109/41.222643
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=222643&isnumber=5803
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