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Optimization by Canonical Analysis in a Radial Basis Function

机译：通过径向基函数的规范分析进行优化

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摘要

Generally, statistical methods and mathematical models are useful for process optimization. Nonetheless, other methods might be used for modeling and optimizing the manufacturing process. Among these, we can mention the neural networks and the Radial Basis Function technique. Hence, a suitable alternative is complementing statistical methods and neural networks as a Hybrid Learning Process. This work applies the Radial Basis Function Canonical Analysis in order to achieve the welding process optimization. One of the most important results is that the Radial Basis Function neural networks along with the Canonical Analysis are really useful methods. These methods are applied for predicting the optimal point, which establishes a reliable method for the process modeling and optimizing. The Canonical Analysis can determine stationary and saddle points, as it was in this case of study, which Canonical Analysis with RBF represented it adequately and can plot a surface and contour lines. Since in this case of study there is a surface that contains a ridge saddle system, also often called minimax. Then the results show that the Canonical Analysis can explore the region with oblique stationary and rising ridge systems.
机译：通常，统计方法和数学模型对于过程优化很有用。尽管如此，其他方法也可以用于建模和优化制造过程。其中，我们可以提及神经网络和径向基函数技术。因此，合适的替代方法是补充统计方法和神经网络作为混合学习过程。这项工作应用了径向基函数规范分析，以实现焊接工艺的优化。最重要的结果之一是径向基函数神经网络和规范分析是真正有用的方法。这些方法被用于预测最佳点，从而为过程建模和优化建立了可靠的方法。如在本研究案例中那样，“规范分析”可以确定固定点和鞍点，使用RBF进行的“规范分析”可以很好地表示出该点，并可以绘制表面和轮廓线。由于在这种研究情况下，存在一个包含脊形鞍形系统的表面，通常也称为minimax。结果表明，典范分析可以探索斜向倾斜和上升脊系统的区域。

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