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", IEEE Trans. Neural Net., June 1990
[IEEE Trans. Neural Netw.]
조회 34645
Status : | Published |
---|---|
Date : | 1990-06 |
Title : | Parallel, Self-Organizing Hierarchical Neural Networks |
Authors : | Daesik Hong and Okan K. Ersoy |
Journal : | IEEE Transactions on Neural Networks |
Abstract : | A new neural-network architecture called the parallel, self-organizing, hierarchical neural network (PSHNN) is presented. The new architecture involves a number of stages in which each stage can be a particular neural network (SNN). At the end of each stage, error detection is carried out, and a number of input vectors are rejected. Between two stages there is a nonlinear transformation of 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 operate simultaneously without waiting for data from other stages during testing. The experiments performed indicated the superiority of the new architecture over multilayered networks with back-propagation training |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...mber=80229 |
Ersoy, O.K.; Hong, D.; , "Parallel, self-organizing, hierarchical neural networks," Neural Networks, IEEE Transactions on , vol.1, no.2, pp.167-178, Jun 1990
doi: 10.1109/72.80229
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=80229&isnumber=2633
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