The purpose of this study is to classify 32 commercial maize hybrids with regard to grain yield stability by using an artificial neural network procedure. The hybrids were evaluated at five locations, in two late growing seasons. Each replication (R1 and R2) of the response variable was used as a network input signal to trigger the network learning process. The underlying network model has a topology consisting of two neurons in the input layer and ten neurons arranged in a two-dimensional grid. The competitive process was induced by the random presentation of an input vector $$x = [x_{1} ,x_{2} , \ldots ,x_{n} ]^{{\text{T}}}$$ from the network training set, without specifying a desired output. A grid neuron y responded best to this stimulus. Thus, the neuron with the shortest Euclidean distance between the input vector and the respective weight vector $$w_{i} = [w_{i1} ,w_{i2} , \ldots ,w_{in} ]^{{\text{T}}}$$ , at moment t, was selected as the winner. The winning neuron indicates the center of a topological neighborhood of cooperative neurons. The adaptive process occurred via applying an adjustment Δwij to the synaptic weights wij during learning, until convergence of the network. The results showed that the classes of hybrids with the same performance pattern across environments were not altered by the network, confirming the high yield stability and satisfactory overall performance associated with higher grain yield means (above 6 t ha−1). The single-cross hybrid 10 (CD-387) stood out at all locations in both years, with unaltered data classification by the network. Therefore, it was considered to be stable in all environments, without performance variation over the years, as well as adaptable.

中文翻译：

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基于 Kohonen 自组织图谱的玉米杂交品种产量稳定性分析

本研究的目的是通过使用人工神经网络程序根据谷物产量稳定性对 32 种商业玉米杂交种进行分类。在两个生长后期的五个地点对杂交种进行了评估。响应变量的每次复制（R1 和 R2）都用作网络输入信号来触发网络学习过程。底层网络模型的拓扑结构由输入层的两个神经元和排列在二维网格中的十个神经元组成。竞争过程是由输入向量 $$x = [x_{1} ,x_{2} , \ldots ,x_{n} ]^{{\text{T}}}$$ 的随机呈现引起的网络训练集，没有指定所需的输出。网格神经元 y 对这种刺激反应最好。因此，输入向量和相应权重向量之间欧氏距离最短的神经元 $$w_{i} = [w_{i1} ,w_{i2} , \ldots ,w_{in} ]^{{\text{T} }}$$ 在 t 时刻被选为获胜者。获胜的神经元表示合作神经元拓扑邻域的中心。自适应过程通过在学习期间对突触权重 wij 应用调整 Δwij 发生，直到网络收敛。结果表明，不同环境中具有相同性能模式的杂种类别没有被网络改变，证实了高产量稳定性和令人满意的整体性能与更高的谷物产量平均值（高于 6 t ha-1）相关。单交叉混合 10 (CD-387) 在这两年的所有位置都脱颖而出，网络的数据分类没有改变。所以，