Prediction Model of Word EERIE Based on BP Neural Network

Authors

  • Xiyu Wang
  • Chenyang Li
  • Zekun Pang

DOI:

https://doi.org/10.54097/hset.v60i.10352

Keywords:

BP neural network algorithm, neural network model, logical regression algorithm

Abstract

This paper uses BP neural network algorithm to determine the difficulty level of the word EERIE by constructing a neural network model, and determine its relationship with the word length, word commonness, letter frequency and other attributes to predict the relevant percentage of the future date, and discusses the uncertainty of the model, the accuracy of the classification model and other characteristics of the data set. In terms of accuracy, the random forest algorithm is the best, 72%, and the k-nearest neighbor algorithm is the worst. In terms of F1 score, logistic regression algorithm performs the best conclusion.

Downloads

Download data is not yet available.

References

Comparison and application of multiple linear regression and BP neural network prediction models Zhang Jingyang Journal of Kunming University of Science and Technology (Natural Science Edition), June 2013, 67-73

Combination evaluation of academic journals based on BP artificial neural network Yu Liping, Statistics and Decision, April 2023, 5-9

Matlab realization of Bp neural network Journal of Shiyun South Hunan University 2010-10-25 86-88

Development of roadway deformation prediction system based on BP neural network model Sheng Jianlong Mining Engineering, June 2007, 63-66

Application Research of BP Neural Network Based on Algorithm Luan Jiuyang Value Engineering June 18, 2012 298-299

Yu Meixia, Dong Gang, Xu Ruxun, Yu Guoli. Precision time base source calibration prediction model based on BP neural network China test: 1-7 [2023-02-19]

BP network realization based on MATLAB neural network toolbox

Feature selection algorithm based on random forest, Yao Dengju, Yang Jing, Zhan Xiaojuan, Journal of Jilin University (Engineering Edition), January 2014, 142-146

Comparative Study of K-Nearest Neighbor and Logistic Regression Classification Algorithms Wan Huifang, Du Yanpu Journal of Luoyang Institute of Technology (Natural Science Edition) 2016-09-25 86-89

Risk assessment and inspection of Wenchuan earthquake landslide based on logistic regression model Xu Chong, Hydrogeology and Engineering Geology, 2013, 03, 104-110

Downloads

Published

25-07-2023