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English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English le...

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Detalles Bibliográficos
Autores principales: Wu, Dan, Shen, Yuanjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423560/
https://www.ncbi.nlm.nih.gov/pubmed/34504519
http://dx.doi.org/10.1155/2021/1890120
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author Wu, Dan
Shen, Yuanjun
author_facet Wu, Dan
Shen, Yuanjun
author_sort Wu, Dan
collection PubMed
description With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.
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spelling pubmed-84235602021-09-08 English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining Wu, Dan Shen, Yuanjun Comput Intell Neurosci Research Article With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition. Hindawi 2021-08-30 /pmc/articles/PMC8423560/ /pubmed/34504519 http://dx.doi.org/10.1155/2021/1890120 Text en Copyright © 2021 Dan Wu and Yuanjun Shen. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Dan
Shen, Yuanjun
English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title_full English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title_fullStr English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title_full_unstemmed English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title_short English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining
title_sort english feature recognition based on ga-bp neural network algorithm and data mining
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423560/
https://www.ncbi.nlm.nih.gov/pubmed/34504519
http://dx.doi.org/10.1155/2021/1890120
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