论文标题
关于识别球体形状的化学分类器的最佳几何和训练策略
On the Optimum Geometry and Training Strategy for Chemical Classifiers that Recognize the Shape of a Sphere
论文作者
论文摘要
在本文中，我们将继续讨论由相互作用的化学振荡器网络构建的数据库分类器。在以前的论文中，我们证明了一个小的定期振荡器网络可以预测$ [0,1] $范围内的三个随机数是否描述了一个位于单位立方体$ [0,1] \ times [0,1] \ times [0,1] $的球内的点，准确的准确性超过$ 80 \％\％。使用进化优化确定网络的参数。在这里，我们应用相同的技术来研究通过选择相互作用振荡器的特定几何形状来提高此问题的分类器精度。我们还讨论了有关培训数据库最佳大小的问题，以进行进化优化以及测试数据集的最小尺寸，以进行分类器准确性的客观评估。
In this paper, we continue the discussion on database classifiers constructed with networks of interacting chemical oscillators. In our previous papers we demonstrated that a small, regular network of oscillators can predict if three random numbers in the range $[0,1]$ describe a point located inside a sphere inscribed within the unit cube $[0,1] \times [0,1] \times [0,1]$ with the accuracy exceeding $80 \%$. The parameters of the network were determined using evolutionary optimization. Here we apply the same technique to investigate if the classifier accuracy for this problem can be improved by selecting a specific geometry of interacting oscillators. We also address questions on the optimum size of the training database for evolutionary optimization and on the minimum size of the testing dataset for objective evaluation of classifier accuracy.
