基于粒子群优化的嗅—味融合技术在啤酒辨识中的应用

Application of Smell and Taste Information Fusion Technology in Classification of Beer Based on Particle Swarm Optimization

摘要:

利用电子鼻/舌融合系统对啤酒香气、滋味进行检测，基于其融合后的嗅/味综合信息实现啤酒的分类。由于传统K均值聚类结果依赖于初始值的选取，且易陷入局部最优，依据融合数据特点提出一种改进的基于粒子群优化的K均值聚类算法，该算法在运行过程中优化了权重系数，随着迭代次数增加同时调整收敛速度，使粒子的搜索更趋于平衡化，同时引入压缩因子，平衡全局与局部矛盾。将该算法与K均值聚类算法进行比较，实验数据证明该算法具有较好的全局收敛性，能克服易陷入局部最优的缺点而收敛于最优解，结果显示：该算法对5种啤酒聚类效果明显，正确率稳定在93.3%。

Abstract:

The flavor of beer is an important means of evaluating its quality. Beer flavor is the integrated embodiment of beer smell and taste information. The aroma and taste of beer were detected by electronic nose and tongue fusion system. Principal component analysis (PCA) was respectively used for reducing the dimension of detected information, and the principal component of test data by electronic nose and tongue were extracted to fuse as the characteristic data. The classification of beer was achieved by smell and taste comprehensive information. Due to the difference in data of sensor array, the traditional K-means algorithm clustering results were depended on the selection of initial value, and it was easy to fall into local optimum. A modified K-means algorithm based on particle swarm optimization was proposed, which was based on the characteristics of fusion data. The weight coefficient was optimized in the course of operation. With the increase of iteration number, the convergence speed was adjusted, the particle search tended to be more balanced. Meanwhile, the compression factor was introduced to balance the global and local conflicts. Compared with K-means algorithm, the modified algorithm had better global convergence in experiments. It also can overcome the disadvantage which was easy to fall into the local optimum, and converge to the optimal solution. The experimental results showed that the clustering effect in five kinds of beer was obvious, and the correct rate was stable at 93.3%.