Abstract
In order to solve the problem of the same type, the same color, the same number of the clone car identification problem, the time credibility and traffic unobstructed degree are the evaluation factors, the membership function of the input vectors was constructed by using the typical function method, and the clone car suspected degree were divided into not suspicious, slight suspicious, suspicious,very suspicious, extreme suspicious of 5 grades. A neural network with 4 layers of nodes is established, which is the input layer, the fuzzy layer, the fuzzy inference layer and the output layer. The simulation results show that the actual output of the network is basically in line with the output of the network forecast, which can meet the requirements of the system.
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Acknowledgment
This research work was supported by the Nature Science Foundation of China, and the project name is “Research on the theory and method of manufacturability evaluation in cloud manufacturing environment”, no. 51405030; the Youth Science Foundation of Jilin Province, no. 20160520069JH.
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Hu, Y., Ren, L., Zhao, H., Wang, Y. (2016). Feature Recognition Based on Fuzzy Neural Network for Clone Car. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_68
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DOI: https://doi.org/10.1007/978-981-10-2663-8_68
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