Oscillatory Neural Networks Based on TMO Nano-Oscillators and Multi-Level RRAM Cells

2015 ◽  
Vol 5 (2) ◽  
pp. 230-241 ◽  
Author(s):  
Thomas C. Jackson ◽  
Abhishek A. Sharma ◽  
James A. Bain ◽  
Jeffrey A. Weldon ◽  
Lawrence Pileggi
Keyword(s):  
Neural Networks ◽  
Multi Level ◽  
Oscillatory Neural Networks

scholarly journals Multi-Level Wavelet Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74973-74985 ◽  
Author(s):  
Pengju Liu ◽  
Hongzhi Zhang ◽  
Wei Lian ◽  
Wangmeng Zuo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Multi Level

Applications of Reservoir Sensitivity Prediction Based on Quantum Neural Networks

2013 ◽  
Vol 712-715 ◽  
pp. 2428-2431
Author(s):  
Wei Zhang ◽  
Jiao Wang ◽  
Jian Bo Xie ◽  
Fei Teng
Keyword(s):  
Neural Networks ◽  
Regression Method ◽  
Core Analysis ◽  
Factors Affecting ◽  
Important Field ◽  
Multi Level ◽  
Main Factors ◽  
Reservoir Protection ◽  
Reservoir Potential ◽  
Reliable Basis

The problem of diagnosing and forecasting reservoir sensitivity damage timely and accurately is always an important field of the reservoir protection research, this paper based on collecting data from core analysis .the main factors affecting reservoir sensitivity are obtained with single variable regression method, established a model of reservoir sensitivity prediction by applying an approach based on multi-level transfer function quantum neural networks. It effectively improved networks convergence and prediction accuracy. The analysis indicates that the model needs fewer parameters, has wider applicability and reliable results (the coincidence rate attains over 91%).and quantitatively reflect reservoir potential sensitivity, thus it can provide reliable basis to reservoir protection.

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