State Performance Evaluation for the Main Pump Bearing of Pump Truck Based on Ant Colony Optimization of Neural Network

Abstract

Abstract: During the process of the state performance evaluation for the key components of mechanical equipment, the convergence speed of BP neural network, genetic neural networks and other hybrid intelligence algorithm is slow and may inevitably meet local minimal problems. According to these problems, a kind of hybrid intelligence algorithm was proposed which combines the global optimization characteristics of ant colony optimization (ACO) and the innings optimization ability of BP neural network. And it is applied in the state performance evaluation for the main pump bearing of pump truck. According to the application results, the ant colony neural network can solve the slowly convergence speed, local minimal problems very well and the accuracy of classification is improved which also reflects the good application prospect.

Key words: main pump bearing of pump truck, state performance evaluation, BP neural network, ACO neural network, global optimal solution

CLC Number:

TP 183

SUN Wang, LI Yan-Ming, DU Wen-Liao, YUAN Jin, LIU Cheng-Liang. State Performance Evaluation for the Main Pump Bearing of Pump Truck Based on Ant Colony Optimization of Neural Network[J]. Journal of Shanghai Jiaotong University, 2012, 46(04): 596-600.

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