Open AccessStudy of BP neural network based on improved particle swarm algorithm
Author(s) -
Xiaoqian Ma,
Liyuan Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2083/3/032041
Subject(s) - particle swarm optimization , artificial neural network , algorithm , robustness (evolution) , autoregressive integrated moving average , position (finance) , swarm behaviour , time series , computer science , series (stratigraphy) , mathematics , artificial intelligence , machine learning , paleontology , biochemistry , chemistry , finance , biology , economics , gene
This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.