韩力群, 何 为, 苏维均, 段振刚. 基于拟脑智能系统的烤烟烟叶分级研究[J]. 农业工程学报, 2008, 24(7): 137-140.
    引用本文: 韩力群, 何 为, 苏维均, 段振刚. 基于拟脑智能系统的烤烟烟叶分级研究[J]. 农业工程学报, 2008, 24(7): 137-140.
    Han Liqun, He Wei, Su Weijun, Duan Zhengang. Grading flue-cured tobacco leaf based on artificial brain intelligent system[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(7): 137-140.
    Citation: Han Liqun, He Wei, Su Weijun, Duan Zhengang. Grading flue-cured tobacco leaf based on artificial brain intelligent system[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(7): 137-140.

    基于拟脑智能系统的烤烟烟叶分级研究

    Grading flue-cured tobacco leaf based on artificial brain intelligent system

    • 摘要: 对烟叶进行检测与分级是控制烟叶质量的重要手段之一。研究烟叶质量的合理表征参数及其自动提取方法,以及烟叶辅助分级系统,是迫切需要解决的问题。应用人工智能方法和计算机技术进行烤烟烟叶自动分级,提出一种借鉴生物脑信息处理结构的烤烟烟叶智能分级系统。该系统由思维模型、感觉模型和行为模型3个子系统构成,分别模拟分级专家的思维智能、感知智能和行为智能,具有学习与记忆、判断与模糊推理、分级决策等多种思维功能,以及图像自动采集、上下位机通信等协调与控制功能。应用该系统进行烟叶分级试验的结果与分级专家分级结果的平均一致率可达到85%,与人工分级水平相当。

       

      Abstract: Measuring and grading flue-cured tobacco leaf is one of the important quality control means. It is imminent to be solved to investigate the reasonable characteristics and the way of automatically abstracting of flue-cured tobacco leaf quality and to design a type of assistant leaf grading system. The application of artificial intelligence and computer technique in automatic grading of flue-cored tobacco leaf was studied. A brain information processing structure was used for reference to design the intelligent system for grading flue-cured tobacco leaf. The system consists of three sub-systems: thinking model, perceived model and behavioral model, to simulate different kinds of intelligence of human grading experts. The system is in possession of multi ‘thinking’ functions such as learning and memory, judging and fuzzy reasoning, and grading decision making. The system also has the functions of coordinating and controlling, such as tobacco leaf image acquisition and the communication between the computer based artificial brain model and the SCM set in the image acquisition system. The grading judgments made by the system are highly consistent with the judgments by grading experts. The average consistent rate reaches 85%.

       

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