Comments on “Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure”

2021 ◽  
Author(s):  
M. Yahyavi ◽  
M. A. Jafarizadeh ◽  
A. Heshmati ◽  
N. Karimi
Keyword(s):  
Neural Network ◽  
Classification Algorithm ◽  
Competitive Learning ◽  
Entanglement Measure

scholarly journals Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

2019 ◽  
Vol 9 (7) ◽  
pp. 1277 ◽  
Author(s):  
Mohammed Zidan ◽  
Abdel-Haleem Abdel-Aty ◽  
Mahmoud El-shafei ◽  
Marwa Feraig ◽  
Yazeed Al-Sbou ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Quantum Computer ◽  
Classification Algorithm ◽  
Input Pattern ◽  
Competitive Learning ◽  
Entanglement Measure ◽  
Computational Power ◽  
Unitary Operators ◽  
Coolant Pump ◽  
Take All

In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.

A novel competitive learning neural network based acoustic transmission system for oil-well monitoring

2000 ◽  
Vol 36 (2) ◽  
pp. 484-491 ◽  
Author(s):  
M. Godoy Simoes ◽  
C. Massatoshi Furukawa ◽  
A.T. Mafra ◽  
J. Cezar Adamowski
Keyword(s):  
Neural Network ◽  
Transmission System ◽  
Competitive Learning ◽  
Oil Well ◽  
Acoustic Transmission

scholarly journals A Study of Image Classification of Remote Sensing Based on Back-Propagation Neural Network with Extended Delta Bar Delta

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Shi Liang Zhang ◽  
Ting Cheng Chang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Geographic Information ◽  
Classification Algorithm ◽  
Classification Model ◽  
Landsat 8 ◽  
Advantages And Disadvantages

This paper proposes a model to extract feature information quickly and accurately identifying what cannot be achieved through traditional methods of remote sensing image classification. First, process the selected Landsat-8 remote sensing data, including radiometric calibration, geometric correction, optimal band combination, and image cropping. Add the processed remote sensing image to the normalized geographic auxiliary information, digital elevation model (DEM), and normalized difference vegetation index (NDVI), working together to build a neural network that consists of three levels based on the structure of back-propagation neural and extended delta bar delta (BPN-EDBD) algorithm, determining the parameters of the neural network to constitute a good classification model. Then determine classification and standards via field surveys and related geographic information; select training samples BPN-EDBD for algorithm learning and training and, if necessary, revise and improve its parameters using the BPN-EDBD classification algorithm to classify the remote sensing image after pretreatment and DEM data and NDVI as input parameters and output classification results, and run accuracy assessment. Finally, compare with traditional supervised classification algorithms, while adding different auxiliary geographic information to compare classification results to study the advantages and disadvantages of BPN-EDBD classification algorithm.

An optimized classification algorithm by BP neural network based on PLS and HCA

2015 ◽  
Vol 43 (1) ◽  
pp. 176-191 ◽  
Author(s):  
Weikuan Jia ◽  
Dean Zhao ◽  
Tian Shen ◽  
Shifei Ding ◽  
Yuyan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Classification Algorithm
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