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Analysis of Task Pricing Under Crowdsourcing Mode

机译:众包模式下的任务定价分析

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In light of the task pricing issues in terms of the new crowdsourcing model, we constructed the Global Optimization Model, Marquardt Algorithm, Back Propagation Neural Network Model and f Fuzzy Neural Network Model at the basis of the cluster analysis, K-means Clustering and other theories and methods. This paper also designed the Fuzzy Neural Network Model by means of MATLAB programming, 1st opt and SPSS software to settle the best pricing scheme, promoting several conclusions such as the completion rate of the original pricing is related to the local population structure and other factors. The task packing mode is recommended to start when the distance between task points is less than 4000 meters and tasks packaging is effective to improve the completion rates etc.
机译:针对新众包模型下的任务定价问题,我们在聚类分析,K-means聚类等基础上,构建了全局优化模型,Marquardt算法,反向传播神经网络模型和f模糊神经网络模型。理论和方法。本文还通过MATLAB编程,1st opt和SPSS软件设计了模糊神经网络模型来确定最佳定价方案,并提出了一些结论,如原始定价的完成率与当地人口结构及其他因素有关。建议在任务点之间的距离小于4000米时开始任务打包模式,并且任务打包有效地提高了完成率等。

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