Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.

中文翻译：

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自动合成循环神经元，用于 CNC 机床操作员的模仿学习

分析工业环境中的时间序列数据需要领域知识和计算机科学专业知识来开发有效的算法。AutoML 方法旨在自动化此过程，减少人为偏见并提高准确性和成本效益。本文应用进化算法来合成针对特定数据集优化的循环神经元。这为 AutoML 框架添加了另一层，针对神经元的内部结构。我们为工业数控机床开发了模仿学习控制系统，以提高操作员的生产力。我们专门检查了两个记录的操作员操作：调整线性进给速率和主轴速度的啮合率。我们将进化神经元的性能与支持向量机和四种常用于时间序列数据的成熟神经网络模型进行比较：简单循环神经网络、长短期记忆、独立循环神经网络和变压器。结果表明，通过进化方法进化的神经元表现出比 LSTM 更低的句法复杂性，并且比其他网络实现更低的错误率。第一个操作操作的错误率降低了 270%，而第二个操作的错误率降低了 20%。我们还表明，我们的进化算法能够创建适合我们数据集特定特征的跳跃连接和门控机制。