Discretization Techniques and Genetic Algorithm for Learning the Classification Method PROAFTN

2009 ◽  
Author(s):  
Feras Al-Obeidat ◽  
Nabil Belacel ◽  
Prabhat Mahanti ◽  
Juan A. Carretero
Keyword(s):  
Genetic Algorithm ◽  
Classification Method

A novel K-means classification method with genetic algorithm

2017 ◽  
Author(s):  
Xuesi Li ◽  
Kai Jiang ◽  
Hongbo Wang ◽  
Xuejun Zhu ◽  
Ruochong Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Classification Method

Two-step classification method based on genetic algorithm for bankruptcy forecasting

2017 ◽  
Vol 88 ◽  
pp. 393-401 ◽  
Author(s):  
Yuri Zelenkov ◽  
Elena Fedorova ◽  
Dmitry Chekrizov
Keyword(s):  
Genetic Algorithm ◽  
Classification Method

scholarly journals Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

2016 ◽  
Vol 8 (8) ◽  
pp. 168781401666347 ◽  
Author(s):  
Milan Eric ◽  
Miladin Stefanovic ◽  
Aleksandar Djordjevic ◽  
Nikola Stefanovic ◽  
Milan Misic ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Production Process ◽  
Parameter Optimization ◽  
Process Parameter ◽  
Classification Method ◽  
Process Parameter Optimization ◽  
New Model ◽  
Model Based ◽  
Abc Classification

scholarly journals Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4596
Author(s):  
Bin Yang ◽  
Mo Huang ◽  
Yao Xie ◽  
Changyuan Wang ◽  
Yingjiao Rong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Classification Method ◽  
Radar Signal ◽  
Circular Array ◽  
Radar Clutter ◽  
Ground Clutter ◽  
Multiple Scenarios ◽  
Standard Genetic Algorithm

The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar’s ground clutter data.

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