Study on the Prediction of Haematococcus pluvialis Population based on Particle Swarm Optimization Neural Network Algorithm

Abstract

In view of the fast convergence and global optimization of particle swarm optimization (PSO), a prediction model based on particle swarm optimization for optimizing BP neural network is proposed. The aim of this paper is to increase the quantity of haematococcus pluvialis. The biomass of haematococcus pluvialis was predicted under different survival conditions by using BP neural network and particle swarm optimization BP neural network. Respectively, the prediction accuracy and fitting degree of the two simulation models are compared and analyzed. The simulation results show that particle swarm optimization BP neural network has a high reliability in the prediction of algae biomass. Compared with the prior BP neural network prediction model, the prediction accuracy is more than 95%. And the fitting degree of the curve is also improved. The algorithm provides the support for the prediction of the biomass of haematococcus pluvialis.