Abstract
The green construction evaluation model is a multi objective and multi qualified model, and the solution has a certain degree of complexity. Often a single algorithm cannot be a good solution to the accuracy of the evaluation system and fault tolerance capabilities and other issues. This paper, through empirical analysis and application of the actual construction of the data depth network model is constructed, in complex models in the abstract condition as factors and as neural network input and output node information and the network optimize. By introducing the ant colony algorithm to train the neural network’s cost function, the paper obtains the high precision model of the green construction evaluation system and the new optimization method for solving the traditional problem. Through the algorithm of organic fusion, the existing algorithms are improved, and the reliability and accuracy of the algorithm are improved. It provides a new theoretical basis and practical model for the green construction evaluation system.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant no. 51504080).
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Jing, M., Kong, J. (2018). Developing Green Construction Evaluation System Based on Deep Neural Network Algorithm. In: Mizera-Pietraszko, J., Pichappan, P. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2016. Advances in Intelligent Systems and Computing, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-60744-3_35
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DOI: https://doi.org/10.1007/978-3-319-60744-3_35
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