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  4. Vol 5, Issue 1, 2024

Research on Word Prediction Based on BP Neural Network

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DOI: 10.23977/autml.2024.050116 | Downloads: 5 | Views: 101

Author(s)

Yanqi Zhang 1, Xinhong Liu 1, Xuanyi Jin 1, Ruisheng Zhang 1, Shuxia Wang 1

Affiliation(s)

1 Beijing Institute of Petrochemical Technology, Beijing, 102617, China

Corresponding Author

Xinhong Liu

ABSTRACT

Today, the Wordle game is exploding all over the world. We study the mechanics of this game, and analyze the game. Smoothness and white noise tests were conducted on the data, and a time prediction model was established for prediction with a high degree of fit. Through analysis of variance, it was found that the properties of words have no effect on numbers in hard mode. BP neural network prediction model was established to predict. After that, the real values were compared with the predicted values for analysis. It is calculated that there is more than 85% confidence in the neural network prediction model. In the end, use K-means clustering algorithm to classify the difficulty of words into simple difficulty, moderate difficulty, and difficulty categories. The difficulty of the word EERIE belongs to the medium difficulty category. Finally, we summarized our results and making recommendations.

KEYWORDS

BP neural network, Time Series, K-Means clustering

CITE THIS PAPER

Yanqi Zhang, Xinhong Liu, Xuanyi Jin, Ruisheng Zhang, Shuxia Wang, Research on Word Prediction Based on BP Neural Network. Automation and Machine Learning (2024) Vol. 5: 125-132. DOI: http://dx.doi.org/10.23977/autml.2024.050116.

REFERENCES

[1] Question C of the 2023 American College Student Mathematical Modeling Competition [EB/OL].The official website of the American College Student Mathematical Modeling Competition, https://www.contest.comap. com/ undergraduate/contests/index.html.
[2] Wang Yan, Applied Time Series Analysis (6th Edition) [M]. Renmin University of China Press, 2022. 
[3] Li D, Lv R, Si G, et al. Hybrid neural network-based prediction model for tribological properties of polyamide6-based friction materials[J]. Polymer Composites, 2017(8):38.
[4] Zhao Yame, Yang Jianguo, Li Beizhi. Implementation of a neural network-based prediction model input parameter configuration method [J]. Computer Measurement and Control, 2005, 13(9):3.
[5] Lv Xiaoling, Song Jie. Big data mining and statistical machine learning [M]. Beijing: People's University of China Press, 2016:114-124.
[6] Han Yuzheng, Li Chunliang, Wang Haitao. Prediction of yield based on BP neural network and fitting[J]. Metallurgical Engineering, 2019, 6(4):232-239.
[7] Hu Yixiang, Huang Yue, Wang Ting, et al. Trend analysis of house price based on neural network prediction model--Haikou and Sanya cities as examples[J]. Fujian Computer, 2018, 34(12):2.
[8] Chen Changsong, Duan Shanxu, Yin Jinjin. Design of a neural network-based prediction model for photovoltaic array power generation [J]. Journal of Electrotechnology, 2009(9):6.
[9] Chang C H, Hung Y H. A Neural Network-Based Prediction Model in Embedded Processes of Gold Wire Bonding Structure for Stacked Die Package [J]. Proceedings of the IEEE, 2009, 97(1):78-83.
[10] Yang T, Tsai T N, Yeh J. A neural network-based prediction model for fine pitch stencil-printing quality in surface mount assembly [J]. Engineering Applications of Artificial Intelligence, 2005, 18(3):335-341.
[11] He Xiaoqun, Multivariate Statistical Analysis (5th Edition) [M]. Renmin University of China Press, 2019.

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