Chaotic neural network-based inventory prediction model and construction method thereof

Abstract

An inventory forecasting model and its construction method based on chaotic neural network. The inventory of finished products is the key factor in precise distribution. If the inventory of finished products is sufficient, accurate delivery will be guaranteed, but the high inventory of finished products will bring a negative impact on the enterprise. The risk is high. On the one hand, it is difficult to process other materials after the original roll is processed into finished products. Once the user does not use it, it is likely to become a waste product. On the other hand, the finished product inventory takes up a large inventory space, which will make Limited storage capacity is getting tighter. The present invention divides the work into two phases. The first is the learning phase. The data of all the distribution users of the sample companies in the past three years are used as samples to establish a model, and these samples are used to learn and adjust the connection weight coefficients of the chaotic neural network, so that the network Realize the given input-output relationship; then the implementation stage, use the trained neural network to obtain the expected effect, establish a perfect calculation model, and realize the reasonable setting of the inventory.

Description

Technical field [0001] The present invention introduces an improved Aihara chaotic neural network model to make reasonable predictions for the ready-to-warehouse distribution of finished products, and specifically refers to an inventory prediction model based on the chaotic neural network and its construction method. Background technique [0002] The inventory of finished products is a key factor in accurate distribution. If the inventory of finished products is sufficient, accurate distribution will be guaranteed. However, the high inventory of finished products will bring high risks to the enterprise. On the one hand, it is very difficult to process the raw rolls into finished products. It is difficult to process other finished products, and once the user does not use them, it is likely to become waste products. On the other hand, finished product inventory occupies a large inventory space, which will make the originally limited storage space more tense. [0003] The past practic...

AI Technical Summary

Problems solved by technology

However, this method has the following disadvantages: (1) The input factor is single

(2) When the input data set does not show chaotic characteristics or the chaotic characteristics are not obvious, the model is as easy to fall into the local minimum as the traditional neural network model

(3) Since the chaotic dynamics are not introduced into the neural network model, the ability of the model to associate and generalize the chaotic dynamics of the system is weak, and the prediction effect is often not ideal

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