Abstract
The data topology structure of uniform experiment design (UD) is too complex to be reasonable regressed. In this paper, the principle and method of distinguish the training data and testing data were described to make a reasonable regression when uniform experiment design combined with support vector regression (SVR). Two equivalent ways which were the smallest enclosing hypersphere perceptron (SEH) and the enclosing simplex perceptron (ES) were provided to discover the topology relationship of the process parameter datum. To give an application, a series of experiments about laser cladding layer quality were conducted by UD to get the relationship of load, velocity and wearing capacity. Results showed that only the testing datum recommended by the two perceptrons got a good forecasting by SVR. Therefore, the two perceptrons could guide experiments with process parameter data of complex topology structure. Further, the application could be extended over a much wider field of experiments.
Highlights
Many researches focus on experimental design combining with nonlinear regression
The important contribution of this paper is to answer such a question: why and how to distinguish the training data and testing data when uniform experiment design combined with nonlinear regression
The distinguishing procedure is to determine if a testing datum lies inside the training datum domain
Summary
Many researches focus on experimental design combining with nonlinear regression. The experiment design. Guangya Zhang [4] used support vector machine to develop the non-linear quantitative structure-property relationship model of the G/11 xylanase based on the amino acid composition, and used the uniform design to optimize the running parameters of SVM. Taguchi approach was used as a statistical design of experimental technique for optimizing the parameters They found that the effect of welding parameters on the welding quality decreased in the order of welding speed, wire feed rate, and laser power. Wang Zhifei et al [15] proposed an optimization design scheme based on orthogonal testing and support vector machines to get relationships between each parameter and product quality features. We choose a case of uniform design combining with support vector machine to proposed two perceptrons to determine the parameters topology boundary (distinguish training data and testing data). An experimental datum set of wear behavior of laser cladding layer is studied to show the function of the two perceptrons
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