A Brief Analysis of the Adaptive Algorithm and Optimization of the BP Neural Network

Abstract

This article first introduces the development of adaptive algorithms, especially the application of deep learning and backpropagation algorithms in adaptive algorithms. Then the backpropagation (BP) neural network including its basic structure and mathematical modeling is analyzed. Special emphasis is placed on the advantages of BP neural network in adaptive algorithms, such as self-regulation ability and ability to handle complex problems. This paper compares the advantages of various algorithms through the performance indicators of various algorithms in predicting early neurological deterioration (END) in patients with acute cerebral infarction (ACI) after intravenous thrombolysis. In addition, the paper discusses the problems that BP neural networks may face in practical applications and optimizes the model by introducing methods such as zero-norm regularization, which can reduce the computational burden in training and inference and can effectively avoid overfitting questions.