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10 July 2020 Evaluation of Financial Performance of Port Enterprises Based on Radial Basis Function Neural Network
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Abstract

Li, J., 2020. Evaluation of financial performance of port enterprises based on radial basis function neural network. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 255–258. Coconut Creek (Florida), ISSN 0749-0208.

An objective, fair, and accurate evaluation of the financial performance of the port enterprises is an important measure to mobilize and improve the enthusiasm of the port enterprises employees and the progress of scientific and technological innovation. This paper proposes a refined evaluation model of the financial performance of port enterprise companies based on the radial basis function (RBF) neural network. The normalized financial performance index data of port enterprise companies are multiplied by the corresponding weight coefficient as the network input, using excellent, good, middle, and F5 grade evaluations as output. The particle swarm optimization algorithm was used to cross-validate the RBF network structure parameters. Through analyzing the RBF network structure and enter and export characteristics, it was found that the trained RBF network weights are highly correlated with the five-level evaluation results and can more accurately distinguish scientific research performance differences than the five-level evaluation results. This weight can be directly used to conduct a detailed evaluation of the financial performance of the port enterprises. This paper promotes the application of RBF networks in the evaluation of financial performance of port enterprise companies and provides new ideas for similar evaluations.

©Coastal Education and Research Foundation, Inc. 2020
Jie Li "Evaluation of Financial Performance of Port Enterprises Based on Radial Basis Function Neural Network," Journal of Coastal Research 106(sp1), 255-258, (10 July 2020). https://doi.org/10.2112/SI106-060.1
Received: 30 November 2019; Accepted: 5 January 2020; Published: 10 July 2020
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