OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Bi-directional
Energy Harvesting
Stochastic Geometry
HetNet
relay networks
FBMC
Equalization
channel capacity
TVWS
CDMA
interference
in-band full-duplex system
Duplex
MIMO
Ultra Low Power
C-V2V
5G
Reliability
SC-FDMA
interference suppression
D-TDD
CLI
indoor positioning
reinforcement learning
RSRP weighting
- Computation offloading
smart factory
Cell-free
multi-access edge computing
estimated position overlapping
—Device-to-device (D2D)
estimated position updating
mMIMO
control overhead
hybrid
NR positioning
Femtocell
Rat-dependent positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
User grouping
power uncertainty
ultra-dense small cell network
mode selection
antenna arrays
5G mobile communication
UFMC
resource block management
inter user interference
WVAN
health care
partial overlap
GFDM
Dynamic TDD
Multi-user Receiver
Number of training blocks
Uplink SCMA system
V2X
Vehicular communication
cross-link interference
LTE-TDD
FS-NOMA
Location-based
user fairness
Mode 3
QR Factorization
Metaheuristics
P-NOMA
non-orthogonal multiple access
dynamic HetNet
spectrum partitioning
and 5G networks.
massive connectivity
non-orthogonal multiple access (NOMA)
overloading
DQN
OTDOA
distributed mode
Communication range
resource selection
maximum likelihood method
Resource sharing
Power allocation
packet delay
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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 64885
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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