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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Sooyong Choi, KyunByoung Ko and Daesik Hong, "A Multilayer Feedforward Neural Network Having N/4 nodes in Two Hidden Layers", IJCNN, Jun. 2001
[IEEE IJCNN]
조회 66804
Status : | Presented |
---|---|
Date : | 2001-07 |
Title : | A Multilayer Feedforward Neural Network Having N/4 nodes in Two Hidden Layers |
Authors : | Sooyong Choi, KyunByoung Ko and Daesik Hong |
Conference : | IEEE International Joint Conference on Neural Networks |
Abstract : | In order to reduce the complexity of a single hidden layer multilayer neural network, a new two hidden layer MFNN (THL-MFNN) with a combined structure of a RBFN and MLPs is proposed, and its associated training method is discussed. The proposed THL-MFNN can be easily constructed, and can be efficiently trained by online recursive methods. The performance of the proposed THL-MFNN with P/4+2=18 hidden nodes and 34 weights is equal to that of an optimum Bayesian equalizer using an RBFN with P=64 hidden nodes and 64 weights. The role of each layer in the proposed THL-MFNN is presented by a theoretical approach, and the feasibility of a more reduced structure is given |
URL : | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=938413 |
Download : | http://mirinae.yonsei.ac.kr/?module=file...94d708d23e |
Sooyong Choi; Kyunbyoung Ko; Daesik Hong; , "A multilayer feedforward neural
network having N/4 nodes in two hidden layers," Neural Networks, 2001.
Proceedings. IJCNN '01. International Joint Conference on , vol.3, no.,
pp.1675-1680 vol.3, 2001
doi: 10.1109/IJCNN.2001.938413
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=938413&isnumber=20319
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