[Matlab] Based on the particle swarm optimization algorithm to optimize the time series forecast of BP neural network (Excel can directly replace the data)

1. Model Principle

Optimizing BP neural network for time series forecasting based on Particle Swarm Optimization (PSO) is a method that combines PSO and BP neural network to improve the performance of BP neural network in time series forecasting tasks. Time series forecasting refers to predicting future time series values ​​based on past time series data. BP neural network is a commonly used forward artificial neural network, but it may fall into a local optimal solution in complex time series forecasting problems. PSO is a global optimization algorithm that can help find better neural network weights and bias values, thereby improving the prediction accuracy of BP neural networks.

The following is the principle of "Optimizing Time Series Forecasting of BP Neural Network Based on Particle Swarm Optimization Algorithm":

1. BP God