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International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)

Xie Yi
Xie Yi
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ASME Press
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To analyze agitation characteristic of screw shaft in Bituminous Mixture Transfer Vehicle (BMTV), a Neural Network (NN) model was established, based on helical blade radius, pitch and rotational speed of screw shaft used as input vector, while asphalt mixture segregation rate used as output vector. To solve the problems of the slow convergence rate and easily falling into local minimum in BP algorithm, PSO algorithm combined with Levenberg-Marquardt algorithm was employed as learning algorithm of the NN. Model of BMTV was designed and manufactured, and model experiment data was applied to simulation experiment of the screw shaft NN model. Simulation and experiment results indicate that the proposed approach not only can overcome the drawbacks of BP algorithm, but also has faster convergence and higher computational precision than original PSO. It also can be applied to building model for agitation characteristics of screw shaft in BMTV, and it is effective.

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