A New Approach of a Possibility Function Based Neural Network

2016 ◽  
pp. 139-150 ◽  
Author(s):  
George A. Anastassiou ◽  
Iuliana F. Iatan
Keyword(s):  
Neural Network ◽  
New Approach

New Approach for 3D Mesh Retrieval Using Artificial Neural Network and Histogram of Features

2018 ◽  
Vol 10 (2) ◽  
pp. 84-94 ◽  
Author(s):  
M. Pershina ◽  
V.S. Bouksim ◽  
K. Arhid ◽  
F.R. Zakani ◽  
M. Aboulfatah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Approach ◽  
3D Mesh ◽  
Artificial Neural

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

scholarly journals Deep neural network for orthogonal frequency division multiplexing systems without cyclic prefix transmission

2018 ◽  
Vol 189 ◽  
pp. 04016
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Orthogonal Frequency Division Multiplexing ◽  
Deep Neural Network ◽  
Cyclic Prefix ◽  
Wireless Transmission ◽  
Frequency Division Multiplexing ◽  
Frequency Division ◽  
Ofdm System ◽  
New Approach ◽  
Multiplexing Systems

Orthogonal frequency division multiplexing (OFDM) is widely used in wired or wireless transmission systems. In the structure of OFDM, a cycle prefix (CP) has been exploited to avoid the effects of inter-symbol interference (ISI) and inter-carrier interference (ICI). This paper proposes a new approach to transmit the signals without CP transmission. Using the deep neural network, the proposed OFDM system transmits data without the CP. Simulation results show that the proposed scheme can estimate the CP at the receiver and overcome the effect of ISI.

scholarly journals Theta function identities from optical neural network transformations

1993 ◽  
Vol 16 (4) ◽  
pp. 805-810
Author(s):  
E. Elizalde ◽  
A. Romeo
Keyword(s):  
Neural Network ◽  
Theta Function ◽  
Heisenberg Model ◽  
Symmetric Functions ◽  
New Approach ◽  
Jacobi Theta Function ◽  
Cylindrically Symmetric ◽  
Optical Neural Network ◽  
Theta Function Identities

We take a new approach to the generation of Jacobi theta function identities. It is complementary to the procedure which makes use of the evaluation of Parseval-like identities for elementary cylindrically-symmetric functions on computer holograms. Our method is more simple and explicit than this one, which was an outcome of the construction of neurocomputer architectures through the Heisenberg model.

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