Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks

2020 ◽  
Vol 3 (10) ◽  
pp. 638-645 ◽  
Author(s):  
Shaochuan Chen ◽  
Mohammad Reza Mahmoodi ◽  
Yuanyuan Shi ◽  
Chandreswar Mahata ◽  
Bin Yuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Density ◽  
Two Dimensional ◽  
Wafer Scale ◽  
Two Dimensional Materials ◽  
Wafer Scale Integration ◽  
Artificial Neural ◽  
Scale Integration

Prediction of 2-D Lift Motions Using Artificial Neural Networks: An Evolutionary Approach

2005 ◽  
Vol 49 (14) ◽  
pp. 1405-1409
Author(s):  
Miguel A. Perez ◽  
Maury A. Nussbaum
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Human Movement ◽  
Two Dimensional ◽  
Modeling Paradigm ◽  
Neural Network Structure ◽  
Artificial Neural

Movement prediction is an important aspect of human simulation, where more efficient and accurate models are needed. Artificial neural networks could potentially serve as a modeling option in this realm. This investigation evaluates the performance of a particular artificial neural network structure in modeling sagittally symmetric two-dimensional lifting and lowering movements. Model performance was evaluated using three training datasets, each consisting of distinct representation levels of the overall dataset. Results are discussed in terms of their practical meaning, and suggestions for future improvements in the modeling scheme are provided. Overall, artificial neural networks show promise as a modeling paradigm for the prediction of human movement.

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