Exploration of Solving Methods and Applications of Neural Network Models in Optimization Problems

Abstract

With the booming growth of artificial intelligence (AI) technology, machine learning (ML) as its core component has shown enormous potential and application value in solving optimization problems. Among numerous ML algorithms, neural networks (NN) have become one of the preferred tools for handling optimization problems due to their high flexibility and powerful learning ability. This article focuses on the application of NN in the field of image recognition, exploring its solution methods and application exploration. Image recognition, as an important branch in the field of AI, aims to recognize and understand information in images through computer algorithms. NN can automatically learn features in images and construct complex classification models by simulating the connections and working modes of human brain neurons. By applying the NN model, the performance of the image recognition system has been significantly improved. NN can accurately recognize objects, scenes, and text information in images, providing strong technical support for fields such as intelligent security, autonomous driving, and medical diagnosis. With the continuous progress of technology and the expansion of application scenarios, NN will play an important role in more fields, promoting the rapid growth of AI technology.