Extension Neural Network-Type 3

2005 ◽  
pp. 503-508
Author(s):  
Manghui Wang
Keyword(s):  
Neural Network ◽  
Type 3 ◽  
Network Type

scholarly journals A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

2021 ◽  
Author(s):  
Kathakali Sarkar ◽  
Deepro Bonnerjee ◽  
Rajkamal Srivastava ◽  
Sangram Bagh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Basic Concept ◽  
Single Layer ◽  
Artificial Neural ◽  
Engineered Bacteria ◽  
Network Type

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible...

Tailoring Unique Neural-Network-Type Carbon Nanofibers Inserted in CoP/NC Polyhedrons for Robust Hydrogen Evolution Reaction

Nanoscale ◽  
2021 ◽  
Author(s):  
Xiaoyan Wang ◽  
Wenxi Zhao ◽  
Yang Fei ◽  
Yanjuan Sun ◽  
Fan Dong
Keyword(s):  
Neural Network ◽  
Hydrogen Evolution ◽  
Carbon Nanofibers ◽  
Hydrogen Evolution Reaction ◽  
Performance Enhancement ◽  
Three Dimensional ◽  
And Performance ◽  
Structural Innovation ◽  
Network Type

Three-dimensional catalysts have attracted great attention in the field of hydrogen evolution reaction (HER). It still, however, remains a great challenge in structural innovation and performance enhancement. Herein, we designed...

Quantitative estimates involving K-functionals for neural network-type operators

2018 ◽  
Vol 98 (15) ◽  
pp. 2639-2647 ◽  
Author(s):  
Danilo Costarelli ◽  
Gianluca Vinti
Keyword(s):  
Neural Network ◽  
Quantitative Estimates ◽  
Network Type

scholarly journals Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Meng-Hui Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fault Tolerant ◽  
Electronic Components ◽  
High Diagnostic Accuracy ◽  
Fault Type ◽  
Correlation Degree ◽  
Network Type

The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1-) based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN) and thek-means based methods.

Evaluating experimental data using expert systems of the neural network type

1995 ◽  
Vol 9 (10) ◽  
pp. 829-831
Author(s):  
E A Gladkov ◽  
A V Malolektov ◽  
G den Owden
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Expert Systems ◽  
The Neural Network ◽  
Network Type

scholarly journals A New Optimizer for Image Classification using Wide ResNet (WRN)

2020 ◽  
Vol 9 (4) ◽  
pp. 1
Author(s):  
Arman I. Mohammed ◽  
Ahmed AK. Tahir
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Optimization Algorithm ◽  
Proper Selection ◽  
Maximum Performance ◽  
Updating Rule ◽  
The Given ◽  
Selection Of ◽  
Network Type

A new optimization algorithm called Adam Meged with AMSgrad (AMAMSgrad) is modified and used for training a convolutional neural network type Wide Residual Neural Network, Wide ResNet (WRN), for image classification purpose. The modification includes the use of the second moment as in AMSgrad and the use of Adam updating rule but with and (2) as the power of the denominator. The main aim is to improve the performance of the AMAMSgrad optimizer by a proper selection of and the power of the denominator. The implementation of AMAMSgrad and the two known methods (Adam and AMSgrad) on the Wide ResNet using CIFAR-10 dataset for image classification reveals that WRN performs better with AMAMSgrad optimizer compared to its performance with Adam and AMSgrad optimizers. The accuracies of training, validation and testing are improved with AMAMSgrad over Adam and AMSgrad. AMAMSgrad needs less number of epochs to reach maximum performance compared to Adam and AMSgrad. With AMAMSgrad, the training accuracies are (90.45%, 97.79%, 99.98%, 99.99%) respectively at epoch (60, 120, 160, 200), while validation accuracy for the same epoch numbers are (84.89%, 91.53%, 95.05%, 95.23). For testing, the WRN with AMAMSgrad provided an overall accuracy of 94.8%. All these accuracies outrages those provided by WRN with Adam and AMSgrad. The classification metric measures indicate that the given architecture of WRN with the three optimizers performs significantly well and with high confidentiality, especially with AMAMSgrad optimizer.

scholarly journals Data adjustment for the purposes of self-teaching of the neural network, and its application for the model-reduction of classification of patients suffering from the ischemic heart disease

2013 ◽  
Vol 61 (2) ◽  
pp. 377-384 ◽  
Author(s):  
Vladimír Konečný ◽  
Oldřich Trenz ◽  
Milan Sepši
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Input Data ◽  
Data Use ◽  
The Neural Network ◽  
Output Neurons ◽  
Practical Instrument ◽  
Network Type ◽  
Data Adjustment

Neural networks present a modern, very effective and practical instrument designated for decision-making support. To make use of them, we not only need to select the neural network type and structure, but also a corresponding data adjustment. One consequence of unsuitable data use can be an inexact or absolutely mistaken function of the model. The need for a certain adjustment of input data comes from the features of the chosen neural network type, from the use of various metrics systems of object attributes, but also from the weight, i.e., the importance of individual attributes, but also from establishing representatives of classifying sets and learning about their characteristics. For the purposes of the classification itself, we can suffice with a model in which the number of output neurons equals the number of classifying sets. Nonetheless, the model with a greater number of neurons assembled into a matrix can testify more about the problem, and provides clearer visual information.

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