دورية أكاديمية

A FOA-Optimized RBF Algorithm-Based Evaluation Research on E-commerce Websites.

التفاصيل البيبلوغرافية
العنوان: A FOA-Optimized RBF Algorithm-Based Evaluation Research on E-commerce Websites.
المؤلفون: Zhang, Xinhe, Hu, Xia, He, Chao, Yu, Chunming
المصدر: Wireless Personal Communications; Oct2018, Vol. 102 Issue 4, p2835-2851, 17p
مصطلحات موضوعية: ELECTRONIC commerce, WEBSITES, ARTIFICIAL neural networks, MATHEMATICAL optimization, ELECTRONIC indexes
مستخلص: By applying the Expert Grading Method and considering the features of e-commerce websites and properties of the indicators, this paper constructs a multi-indicator hierarchical structure for the competitiveness index evaluation of e-commerce website as well as built an indicator system for the evaluation. This system can be used to measure the competitiveness index of such a website and quantify its competitiveness. Then Radial Basis Function (RBF) Neural Network Algorithm (NNA) is adopted to evaluate and research the competitiveness indexes of e-commerce websites. Against the problems therein, this paper tries to improve the RBF NNA with Fruit Fly Optimization Algorithm (FOA). Through the simulation and contrast of examples, FOA-RBF algorithm obviously works better than RBF NNA in measuring and evaluating the competitiveness indexes of such websites. Therefore, it is verified that the algorithm proposed by this paper is both effective and reliable. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
قاعدة البيانات: Complementary Index
الوصف
تدمد:09296212
DOI:10.1007/s11277-018-5310-8