Method for realizing formulated product sensing index prediction based on M5' model tree

Abstract

To overcome the defects in prior art, in this invention, constructing a basic decision tree; then, in pruning stage, building LRM for every node, while eliminating some sub-trees to prevent over fitting; finally, reducing the nonlinearity on model fragmenting points as small sample with smoothing process to obtain the correlation model and the express prediction system. Wherein, the key lies on that it introduces the M5' model into the prediction process.

Description

technical field [0001] The invention relates to a method flow for data prediction and analysis, in particular to establishing a method for predicting sensory quality indicators in the production process of formula products. Background technique [0002] In the manufacturing industry of existing formula products, such as cigarettes for daily consumption, food, spices, food additives, etc., the formula components and their proportions used are related to the quality and grade assessment of raw materials and finished products. For example, cigarette products are usually evaluated by indicators such as fragrance style, irritation, strength, etc., so as to indicate their different grades to consumers. To study the relationship between the formula components of formula products and physical, chemical and sensory indicators, and then improve the quality of product manufacturing and the evaluation of grade products is a task with a large amount of data processing. It has always been...

AI Technical Summary

Problems solved by technology

In practical applications, how to estimate the number of neurons in the hidden layer has always been the difficulty and key to determine the structure of BP network, and there is no strict theoretical basis at present.

In addition, the data composition conditions for the formation of sensory indicators are relatively complex, such as the place of origin, climate, soil and other factors have a greater impact on the indicator data

Different prediction models should be established for different data, which will lead to many problems such as heavy workload and difficulty in adjusting parameters.

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