Optimization by Canonical Analysis in a Radial Basis Function

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.In this way, the RBF neural network with Canonical Analysis could be an alternative method for analyzing data, whenever the Hybrid Learning Process is adequate or satisfies the test assumption and fulfills the evaluation criteria.In this case of study, validation is represented by the Hybrid Learning Process (Radial Basis Function with Canonical Analysis) presenting an excellent effectiveness.As a conclusion we can say that the resulting Radial Basis Function has improved the model accuracy after using the Canonical Analysis.