Novel tire inflating system using extreme learning machine algorithm for efficient tire identification

Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process

Novel tire inflating system using extreme learning machine algorithm for efficient tire identification

Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process. © 2017 IEEE.Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 201