Vehicle Framework Based On Wavelet Neural Network Artificial Snake Reconstruction Technology

In the railroad industry of our country, the appearance of high-speed train and railway system's speed level increasing induce the derail event increasing. Derailed brings huge losing to human being and society property which menace the safety of railway transportation. In order to get the safety speed of the train, measure and rebuild the train crawl wave which is regard as the actuator of Vehicle-Bridge System is the most important.Firstly, the paper summarize the Vehicle-Bridge System,the factor which results system's vibration and the effect of system's actuator to train's safety in briefly, get the result that the key to predict the response of the Vehicle-Bridge System is to get the artificial crawl wave which is very close to the real crawl wave by measured. Then particular introduce the Monte-Carlo method which is the main means to rebuild artificial crawl wave nowadays. In method research, the random simulation of artificial crawl wave based on Monte-Carlo method only used variance while neglect many other useful information such as frequency and probability, it make chance average of frequency and phase, lead the result is not close the real crawl wave. This paper put out a new method to rebuild the crawl wave, construct the wavelet neural network which contain the roulette wheel select mechanism , process the data which is measured on GuangShen railway at speed 160km/h by wavelet neural networks, utilize the amplitude, frequency and probability adequately, and rebuild the crawl wave by computer. The result show this method can rebuild the crawl wave well.Compare to the Monte-Carlo method, the method based on wavelet neural network conquer the flaws such as chance average and wave saltation, the wave rebuild by wavelet neural network can be close to the real measured crawl wave. Meanwhile this method can also apply in rebuild high-speed train's crawl wave.