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Prediction Model of Supply Chain Demand Based on Fuzzy Neural Network with Chaotic Time Series

机译:基于模糊神经网络的混沌时间序列的供应链需求预测模型

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In order to quickly determine and control the chaotic oscillation in supply chain system, to enhance the prediction accuracy of supply chain demand, and ensure the stability of supply chain systems, using fuzzy neural networks based on chaotic time series, sub-phase space is rebuilt by the demand time-series of supply chain system. Calculating the phase-space saturated embedding dimension and the largest Lyapunov index. Prediction model of supply chain demand has been built by fuzzy neural network based on a chaotic time series. The chaotic phenomena can be judged in supply chain system. Supply chain demand prediction controller has been designed based on fuzzy neural network. The simulating results show that fuzzy neural network with chaotic time series is feasible and effective on prediction of supply chain demand.
机译:为了快速确定和控制供应链系统中的混沌振荡,提高供应链需求的预测准确性,并确保供应链系统的稳定性,采用基于混沌时间序列的模糊神经网络,重建子相空间供需时间系列供应链系统。计算相空间饱和嵌入维度和最大的Lyapunov指数。基于混沌时间序列的模糊神经网络建立了供应链需求预测模型。可以在供应链系统中判断混沌现象。基于模糊神经网络设计了供应链需求预测控制器。模拟结果表明,具有混沌时间序列的模糊神经网络是可行且有效的供应链需求预测。

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