2020
DOI: 10.1007/s10681-020-02683-x
|View full text |Cite
|
Sign up to set email alerts
|

Yield stability analysis of maize hybrids using the self-organizing map of Kohonen

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
3
0
1

Year Published

2021
2021
2023
2023

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(4 citation statements)
references
References 16 publications
0
3
0
1
Order By: Relevance
“…The set of all possible SQL queries must be divided into an unknown number of clusters. For this purpose, the device of Kohonen's Self-Organizing Maps (hereinafter SOM) is used [15,16,17]. The SOM training algorithm is developed, based on the use of a rational value of the winning neuron topological neighborhood width, which makes it possible to configure the neural network to prevent its overfitting.…”
Section: Methodsmentioning
confidence: 99%
“…The set of all possible SQL queries must be divided into an unknown number of clusters. For this purpose, the device of Kohonen's Self-Organizing Maps (hereinafter SOM) is used [15,16,17]. The SOM training algorithm is developed, based on the use of a rational value of the winning neuron topological neighborhood width, which makes it possible to configure the neural network to prevent its overfitting.…”
Section: Methodsmentioning
confidence: 99%
“…Initialize the cluster center vector Z = ðz 1 , z 2 ,⋯,z c Þ, each vector in this cluster center vector set is also a p-dimensional vector, and initialize the number of training T = 0, the maximum number of training is T max , and the initial weighted power exponent of the degree of membership is K 0 ðK 0 > 1Þ. Set the termination error of the iteration as ε > 0 [15,16].…”
Section: Application Of Kohonen Neural Network In Sports Clustermentioning
confidence: 99%
“…С использованием полученного ограниченного числа значимых параметров множество всех возможных запросов к базам данных необходимо разбить на некоторое число кластеров, отличающихся ресурсоемкостью выполнения SQL-операторов. С этой целью предлагается использовать аппарат самоорганизующихся карт Кохонена (Self-Organization Maps, SOM) [Sinha, 2010;Sakkari, 2020;Clovis et al, 2020], на основе которого разработан алгоритм обнаружения ресурсоемких запросов.…”
Section: обоснование числа параметров учитываемых при обнаружении ресурсоемких запросовunclassified