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Optimizing Elevator Dispatching Strategy Based on Perceptron Algorithm

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DOI: 10.23977/jaip.2022.050101 | Downloads: 11 | Views: 187

Author(s)

Wei Zhang 1, Ziyu Chen 1, Jiapeng Cai 1, Min Li 1, Yingying Li 1

Affiliation(s)

1 Department of Electrical and Computer Engineering, Nanfang College, Guangzhou, 510900, China

Corresponding Author

Ziyu Chen

ABSTRACT

With the rapid development of high-rise buildings, the application of elevator is increasing day by day. In order to solve the problem of more reasonable and efficient operation of elevator in the peak of passenger flow, this paper identifies the characteristics of passenger flow by counting the number of up calls and down calls in a period of time, which provides a basis for the optimization of elevator scheduling and coordination control. In this thesis, the perceptron model in machine learning is used to realize the elevator traffic pattern recognition. Through the training of the existing data to construct the traffic pattern recognition model, and then through the recognition of the model to verify the test data, finally we can achieve the correct identification of the elevator traffic pattern.

KEYWORDS

Machine learning, Perceptron, Pattern recognition

CITE THIS PAPER

Wei Zhang, Ziyu Chen, Jiapeng Cai, Min Li, Yingying Li, Optimizing Elevator Dispatching Strategy Based on Perceptron Algorithm. Journal of Artificial Intelligence Practice (2022) Vol. 5: 1-6. DOI: http://dx.doi.org/10.23977/jaip.2022.050101.

REFERENCES

[1] Bao Hai. Research on traffic pattern recognition and multi-objective optimization group control algorithm of elevator group control system based on fuzzy neural network [D]. Tongji University, 2007.
[2] Zheng ting. Talking about how to improve the operation efficiency of elevator in peak period [J]. Heilongjiang Science and technology information, 2009 (25): 32.
[3] Understanding neural networks. Here are all the nouns you need to know [EB/OL]. (2017-11-04) [2021-4-25].
[4] Chen Ming. Matlab neural network principle and example refinement [M]. Tsinghua University Press, 2013.

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