遗传RBF神经网络在卷烟香气质量评定中的应用

    Radial Basis Function Neural Network Based on Genetic Algorithms for Evaluating Cigarettes Aroma

    • 摘要: 卷烟香气质量感官评定结果的准确性往往难以保证,因此研究准确、客观的评定方法是必要的。运用遗传RBF神经网络研究了基于气敏传感器阵列的卷烟香气质量评定方法。实例表明,该方法是可行的,所给出的遗传学习算法是有效的。为进一步开展卷烟香气质量客观评定方法的研究奠定了基础。

       

      Abstract: The aroma quality of cigarettes is often evaluated by sense of experts in quality of cigarettes. The accuracy of evaluation results is hard to be guaranteed, so it is necessary to research accurate and objective evaluation method now. In this paper, the evaluation method on aroma of cigarettes is deeply researched using Radial Basis Function (RBF) neural network based on genetic algorithms and smell sensor array. Experimental results show clearly that: this method is feasible, the learning algorithm given by this paper for RBF neural network is effective. Some foundations for going further into the objective evaluation method of cigarettes aroma are laid.

       

    /

    返回文章
    返回