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17 December 2020 Comprehensive Evaluation Model of Maritime Industrial Economic Activities Based on an AHP and BP Neural Network
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Abstract

Yang, M., Chen, J., and Zhou, Y., 2020. Comprehensive evaluation model of maritime industrial economic activities based on an AHP and BP neural network. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 178–182. Coconut Creek (Florida), ISSN 0749-0208.

Based on a comprehensive evaluation of maritime industrial economic activities that are difficult to quantify and with which subjective factors interfere strongly, this paper uses the analytic hierarchy process and BP neural network to build a comprehensive evaluation model. Taking a marine enterprise as an example, the first-level and second-level indicators of the economic activities of the enterprise are adopted. The model determines the input weights of the first-level indicators according to the expert scoring method, multiplies the 27 second-level indicators under the first-level indicators by the weights of the first-level indicators as the input indicators of the model, and establishes training based on a Bayesian regularization and gradient descent method. The three-layer BP neural network application model has a better chance of improving the problem that subjective factors dominate and lead to lower evaluation accuracy of industrial economic activities. Taking a comprehensive evaluation experiment of industrial economic activities of a plant in 2020 as an example, the experimental results prove that the evaluation results of the model have guiding significance for the analysis and evaluation of industrial economic activities.

©Coastal Education and Research Foundation, Inc. 2020
Ming Yang, Jia Chen, and Yalin Zhou "Comprehensive Evaluation Model of Maritime Industrial Economic Activities Based on an AHP and BP Neural Network," Journal of Coastal Research 111(sp1), 178-182, (17 December 2020). https://doi.org/10.2112/JCR-SI111-030.1
Received: 18 March 2020; Accepted: 17 July 2020; Published: 17 December 2020
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