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.
Highlights
As a populous country, China's demand for metal, coal, steel, electricity and other energy is increasing, along with the mining of energy, and led to the expansion of the fan market, so the requirements for the performance of the fan is getting higher and higher
Yao Jingyu[1] predicted the aerodynamic noise of the axial flow fan by combining BP neural network and numerical simulation, and verified through the test set that the loss value of the noise prediction method by combining neural network with numerical simulation was 0. 255%, indicating the feasibility of this method
The improved Particle Swarm Optimization (PSO)-BP neural network noise prediction model of centrifugal fan established in this paper adopts 4-8-1 structure
Summary
China's demand for metal, coal, steel, electricity and other energy is increasing, along with the mining of energy, and led to the expansion of the fan market, so the requirements for the performance of the fan is getting higher and higher. Wei Zhen[2] et al used Elman neural network to predict wind farm noise, and the simulation results showed that the model could fit well, and the prediction results were in line with the actual situation, which could provide a certain reference for wind farm noise prediction. The simulation results showed that the BP neural network could well reflect the variation trend of noise, which was basically consistent with the actual trend, and had a strong reference significance for noise control. Cheng Jing[6] et al predicted fan noise by combining regression analysis and BP neural network, and applied the model to the actual test of a wind airport in Xinjiang, which achieved good results, but the single BP neural network was unstable. The variation trend of predicted noise is basically consistent with the actual trend, so it is feasible to use BP neural network in the prediction of centrifugal fan noise
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