基于Tiebout模型和改进人工神经网络的云服务定价预测方法  

Cloud Service Pricing Forecasting Method Based on Tiebout Model and Improved Artificial Neural Network

作　　者：王鹤琴[1,2] WANG He-qin(Anhui Vocational College of Police Officers,Hefei Anhui 230031,China;Institute of Computer Science and Technology,Anhui Normal University,Wuhu Anhui 241000,China)

机构地区：[1]安徽警官职业学院信息管理系,安徽合肥230031 [2]安徽师范大学计算机科学与技术研究所,安徽芜湖241000

出　　处：《菏泽学院学报》2023年第5期20-26,共7页Journal of Heze University

基　　金：国家自然科学基金(61572036);教育部哲学社会科学研究重大课题攻关项目(20JZD026);安徽省高校学科(专业)拔尖人才学术资助项目(gxbjZD2022147);安徽高校自然科学研究重点项目(KJ2020A1058,2022AH050602)。

摘　　要：随着云服务市场的不断扩大,制定合理的价格对于云服务提供商和用户越来越重要.对目前现有的定价模型进行研究时,发现较多模型考虑的因素不全面,同时也未注意到各因素与定价之间的关系.在Tiebout模型基础上提出了一种利用布谷鸟算法改进人工神经网络对云服务定价预测的方法(CS-ANN).与BP神经网络、RBF神经网络、ANN人工神经网络预测定价和改进人工神经网络分别进行比较,实验表明基于蒂布特模型和改进的神经网络定价优化模型的预测精度更高.With the continuous expansion of the cloud service market,establishing reasonable pricing has become increasingly important for both cloud service providers and users.When researching existing pricing models,it has been observed that many models do not consider a comprehensive range of factors,and the relationship between these factors and pricing has often been overlooked.In this paper,a Cloud Service Pricing Forecasting Method(CS-ANN)is proposed based on the Tiebout model.This method utilizes the cuckoo algorithm to enhance cloud service pricing prediction using artificial neural networks.Comparative experiments with the BP neural network,RBF neural network,artificial neural network price prediction,and improved artificial neural network show that the prediction accuracy of the Tiebout model-based and improved neural network pricing optimization model is higher.

关 键 词：人工神经网络 布谷鸟算法 TIEBOUT模型 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]