Cigarette sensing appraise and flume index immune neural net prediction method

A sensory evaluation, neural network technology, applied in measurement devices, special data processing applications, instruments, etc., can solve problems such as not being used well, improve learning speed and accuracy, reduce complexity, and optimize weights and structures. Effect

Inactive Publication Date: 2009-04-29
HARBIN ENG UNIV
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Problems solved by technology

Tobacco companies have accumulated a lot of valuable experience and data in the chemical compo...

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  • Cigarette sensing appraise and flume index immune neural net prediction method
  • Cigarette sensing appraise and flume index immune neural net prediction method
  • Cigarette sensing appraise and flume index immune neural net prediction method

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Embodiment Construction

[0025] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0026] The immune neural network prediction method of cigarette sensory evaluation smoking and smoke index is as follows:

[0027] 1. Detect the physical and chemical indicators and smoke analysis indicators of single-material cigarettes and finished cigarettes, organize industry experts to evaluate single-material cigarettes and finished cigarettes, and enter the obtained data into the database.

[0028] 2. The system automatically eliminates wrong or specific samples, and normalizes the sample data.

[0029] 3. Combining the experience of experts in the field, divide single-material cigarettes or finished cigarettes into several groups according to different styles; then apply immune network feature mapping to cluster the sample data of physical and chemical indicators of cigarettes, and complete all single-material cigarettes and cigarettes in the database. Fi...

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Abstract

The invention provides a method for predicting an immune neural network of a cigarette sensory smoke panel test and smoke indexes. The method comprises the following steps: detecting analysis indexes and storing the obtained data into a database; normalizing sample data; dividing single material cigarettes and cigarette products into a plurality of groups according to styles; finishing the final ranking of all the single material cigarettes and cigarette products in the database; establishing corresponding immune neural networks for N physical and chemical index samples of different types of the single material cigarettes of the cigarette products respectively; sending the normalized sample data into the corresponding immune neural networks; using an immune algorithm to optimize the network weight and structure until ranking targets are reached; storing the network weight and the structure in a knowledge base; and judging whether the sample data to be analyzed is new and unclassified. The method has the functions of higher ranking accuracy, more accurate mapping relations between the physical and chemical indexes of all kinds of cigarettes and the sensory smoke panel test and the smoke indexes, fewer manual smoke panel tests and detecting times, auxiliary formulation design, and improvement of work efficiency.

Description

(1) Technical field [0001] The invention relates to a computer measuring method for cigarette sensory and smoke indexes. (2) Background technology [0002] Tobacco leaves contain many chemical components; the interaction of various chemical components in the smoking process stimulates people's senses of taste, smell, and touch, all of which are extremely complex. The experience of experts in tobacco leaf evaluation and smoking is very valuable, but it also has obvious uncertainties. At present, the domestic tobacco industry mostly adopts traditional statistical (such as multiple regression) analysis for the correlation between chemical components of tobacco, smoke analysis indicators, and sensory quality evaluation indicators. The model constructed by this method is only applicable to the analyzed tobacco; once there is a new sample, the 'recipe model' has to be regressed, the solution process is complicated, and it is difficult to quickly modify the 'recipe model'. The ex...

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Application Information

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IPC IPC(8): G01N33/00G01N35/00G06F17/00
Inventor 莫宏伟安毅徐立芳冯天瑾丁香乾王志军周长生李辉马琳涛管凤旭
Owner HARBIN ENG UNIV
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