Auto anti-lock braking system bench test results classification model based on neural network

Auto anti-lock braking system (ABS) bench test is a safe, high-efficiency and low-cost method for ABS performance detection. The key parameters such as slip ratio, adhesion coefficient utilization rate and deceleration can be obtained quickly. In this paper, a classification model based on neural ne...

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Bibliographic Details
Main Authors: Hao Ru-ru, Zhao Xiang-mo, Xu Zhi-gang
Format: Conference Proceeding
Language:English
Subjects:
Adhesives
auto ABS bench test
data analysis
Educational institutions
Mathematical model
neural network
Neurons
Training
Vehicles
Wheels
Online Access:Request full text
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Summary:Auto anti-lock braking system (ABS) bench test is a safe, high-efficiency and low-cost method for ABS performance detection. The key parameters such as slip ratio, adhesion coefficient utilization rate and deceleration can be obtained quickly. In this paper, a classification model based on neural network for ABS bench test results was established. And the detailed BP network structure design process was presented. BP neural network self-learning ability was used to analyze the bench test data. A large number of model mapping relationships were summarized and stored in the networks. MATLAB simulations show that the BP neural network model can classify the ABS bench test results correctly for a variety of experimental conditions.
DOI:10.1109/ICECC.2011.6066500