Noise Prediction of Centrifugal Fan Based on Improved Nenral Network

School of Mechanical and Eletrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China

^a 846013116@qq.com
^* Corresponding author:
^b jiangshuxia_2004@126.com.

Abstract

In order to solve the problems that the prediction accuracy of the traditional centrifugal fan is low and the cost is high, a noise prediction model for centrifugal fan based on improved particle swarm optimization (IPSO) optimized BP neural network was presented. The initial weights and thresholds of BP neural network were optimized by using IPSO. The 17 parameters were collected by the liancheng company and be used to establish the regression equation to obtain the standard regression coefficient. The importance of the fan parameters was ranked and four key characteristic parameters were determined as input values by the optimization algorithm to build the IPSO-BP centrifugal fan noise prediction model. After comparative study, IPSO-BP model has better prediction effect than PSO-BP model and BP model, and the prediction error is only 0. 97%. The research shows that IPSO-BP model can effectively shorten the fan design period and save the design cost.