Prediction model of Wordle report based on GA-BP neural network

Abstract

Natural Language Processing (NLP) plays a vital role in artificial intelligence, enabling machines to understand and generate human language. This paper proposes a novel approach to improve NLP tasks by integrating a Genetic Algorithm-based BP neural network model. The model combines the strengths of genetic algorithms and neural networks. Genetic algorithms offer effective search capabilities, while the BP neural network provides a flexible learning framework. By integrating these techniques, the proposed model aims to overcome limitations of traditional NLP models. It effectively handles the complexity of natural language data, provides efficient training by avoiding local optima, and exhibits enhanced generalization capabilities for unseen data. Extensive experiments on benchmark NLP datasets validate the effectiveness of the proposed model. Results demonstrate its superiority over state-of-the-art NLP models in terms of accuracy, efficiency, and robustness. This paper presents a novel approach to enhance NLP tasks by integrating a genetic algorithm-based BP neural network model. The model shows promising results in various NLP applications and offers advantages over traditional approaches. This research contributes to the advancement of NLP techniques, facilitating more accurate and efficient language processing systems.