Prediction of Wordle Game based on RBF Neural Network Modeling

Biao Guo ； Jinyue Li ； Xuetao Wang

《International Core Journal of Engineering》 9卷10期 (2023/10) Pp. 156-162

https://doi.org/10.6919/ICJE.202310_9(10).0020

摘要

Wordle is a popular puzzle currently offered daily by the New York Times, where the number of times a player completes the game uses affects the sense of experience and thus the number of players. In this paper, we use an RBF neural network to predict the distribution of the percentage associated with the number of times a task is completed in terms of the number of times it is used, dividing each word into five letters and coding the pairs, and taking into account the effect of the date, we coded the date from in this paper as a way of investigating the effect of the word itself, as well as the date, on the distribution of the percentages. The prediction results show that the RMSE and R2 of the model are 0.2993 and 0.9021, respectively, the model has high accuracy, and the results are highly credible; this paper takes the word EERIE as an example, and predicts the percentage distribution of the word as 0.34, 3.92, 17.82, 30.99, 26.18, 15.09, and 5.39.

關鍵字

RBF Neural Network Model ； Pedictive Model ； Wordle

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