量子神经网络在心电图分类中的应用

将量子叠加的概念引入前向神经网络，提出了量子神经网络的计算模型。量子神经网络分类器是将量子迁移（量子间隔）概念引入前向神经网络，在隐含层和输出层借鉴量子理论中的量子迁移（量子间隔）思想，神经元采用多个激励函数的叠加，形成对特征空间的多级划分，在训练过程中，量子神经元能够根据需要伸展或坍塌。当输入模糊信息时，该算法可以学习数据集中的不精确性或不确定性，具有较高的分类精度。将该算法应用于心电图诊断中，结果表明具有较好的分类效果和较快的训练速度。

Neural Network Algorithm and Application of the ECG Classification

Objective In order to guide the standardized cultivation on Chrysanthemum morifolium in Futianhe region, the effect of agronomic measure of fertilization and plastic film mulching on the yield and quality of C. morifolium flower was studied. Methods Field test was adopted to study the effect of fertilization and plastic film mulching on C. morifolium, and spectrophotometric analysis and HPLC method were used to determine the flavonoids and chlorogenic acid in C. morifolium flower, respectively. Results Applying only with nitrogenous fertilizer in the cultivation decreased the yield and quality of C. morifolium flower. And the yield, quality, and production value of C. morifolium flower would be increased with the application of organic manure and inorganic elements, such as N, P, K, B, and Zn. The compound fertilizer with high N content could remarkably increase the yield of C. morifolium flower, but it would lower the quality. However, the total flavonoids and chlorogenic acid content of C. morifolium flower would be remarkably improved, when it was fertilized with high K compound fertilizer. Moreover, the maturity of C. morifolium could be shortened with plastic film mulching, and the yield also be remarkably increased. Conclusion In the cultivation of C. morifolium in Futianhe, it is strongly recommended to adopt plastic film mulching and to fertilize with the special compound fertilizer with high K content.