scholarly journals EXPERIMENTAL STUDY ON STRESS & DEFORMATION BEHAVIOR OF A WOODEN SINGLE-LAYER SPACE FRAME WITH A COMPRESSIVE STRAIN INCLINED TO THE GRAIN

1995 ◽  
Vol 60 (473) ◽  
pp. 117-126 ◽  
Author(s):  
Kouichi MATSUNO ◽  
Shigeru AOKI
Keyword(s):  
Experimental Study ◽  
Deformation Behavior ◽  
Compressive Strain ◽  
Single Layer ◽  
Space Frame ◽  
Stress Deformation

Single-layer folding in marble and limestone: An experimental study

1984 ◽  
Vol 108 (1-2) ◽  
pp. 155-172 ◽  
Author(s):  
V.K. Gairola ◽  
H. Kern
Keyword(s):  
Experimental Study ◽  
Single Layer

Numerical and experimental study of an active control logic for modifying the acoustic performance of single-layer panels

2021 ◽  
pp. 116608
Author(s):  
Francesco Ripamonti ◽  
Anthony Giampà ◽  
Riccardo Giona ◽  
Ling Liu ◽  
Roberto Corradi
Keyword(s):  
Experimental Study ◽  
Active Control ◽  
Single Layer ◽  
Control Logic ◽  
Acoustic Performance

NUMERICAL SIMULATION AND EXPERIMENTAL STUDY OF A HARDWARE PULSE NEURAL NETWORK WITH MEMRISTOR SYNAPSES

2021 ◽  
Vol 7 (2) ◽  
pp. 223-235
Author(s):  
Alexander N. BUSYGIN ◽  
Andrey N. BOBYLEV ◽  
Alexey A. GUBIN ◽  
Alexander D. PISAREV ◽  
Sergey Yu. UDOVICHENKO
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Experimental Study ◽  
Single Layer ◽  
Electrical Circuit ◽  
Learning Curves ◽  
Spiking Neural Network ◽  
New Associations ◽  
Strong Artificial Intelligence ◽  
Comparison Of The Results

This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in the form of a memory matrix in the mode of a single-layer perceptron synapses. The perceptron can be considered as the first layer of a biomorphic neural network that performs primary processing of incoming information in a biomorphic neuroprocessor. The obtained experimental and simulation learning curves show the expected increase in the proportion of correct classifications with an increase in the number of training epochs. The authors demonstrate generating a new association during retraining caused by the presence of new input information. Comparison of the results of modeling and an experiment on training a small neural network with a small crossbar will allow creating adequate models of hardware neural networks with a large memristor-diode crossbar. The arrival of new unknown information at the input of the hardware spiking neural network can be related with the generation of new associations in the biomorphic neuroprocessor. With further improvement of the neural network, this information will be comprehended and, therefore, will allow the transition from weak to strong artificial intelligence.

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