A multi-layer-perceptron neural network hardware based on 3D massively parallel optoelectronic circuits

2003 ◽  
Author(s):  
K.D. Maier ◽  
C. Beckstein ◽  
R. Blickhan ◽  
W. Erhard ◽  
D. Fey
Keyword(s):  
Neural Network ◽  
Massively Parallel ◽  
Multi Layer Perceptron ◽  
Neural Network Hardware

Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network

2021 ◽  
Author(s):  
Yu Yao ◽  
Xiaoxiao Yang ◽  
Sai Hin Lai ◽  
Ren Jie Chin
Keyword(s):  
Neural Network ◽  
Solitary Wave ◽  
Multi Layer Perceptron ◽  
Fringing Reefs ◽  
Run Up

FPGA acceleration on a multi-layer perceptron neural network for digit recognition

2021 ◽  
Author(s):  
Isaac Westby ◽  
Xiaokun Yang ◽  
Tao Liu ◽  
Hailu Xu
Keyword(s):  
Neural Network ◽  
Multi Layer Perceptron ◽  
Digit Recognition ◽  
Fpga Acceleration

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

Neural network implementations and speed-up on Massively Parallel machines

1992 ◽  
Vol 35 (1-5) ◽  
pp. 747-754
Author(s):  
M.E. Azema-Barac ◽  
A.N. Refenes
Keyword(s):  
Neural Network ◽  
Parallel Machines ◽  
Massively Parallel ◽  
Speed Up

Massively parallel architectures for large scale neural network simulations

1992 ◽  
Vol 3 (6) ◽  
pp. 876-888 ◽  
Author(s):  
Y. Fujimoto ◽  
N. Fukuda ◽  
T. Akabane
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Parallel Architectures ◽  
Massively Parallel ◽  
Massively Parallel Architectures ◽  
Network Simulations
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