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<span style="font-size: 10.5pt; font-family: &quot;Times New Roman&quot;; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The use of support vector machine (SVM) for function approximation has increased over the past few years. Unfortunately, the practical use of SVM is limited because the quality of SVM models heavily depends on a proper setting of SVM hyper-parameters and SVM kernel parameters. Therefore, it is necessary to develop an automated, reliable, and relatively fast approach to determine the values of these parameters that lead to the lowest generalization error. This paper presents two SVM parameter optimization approaches, i.e. GA-SVM and PSO-SVM. Both of them adopt a objective function which is based on the leave-one-out cross-validation, and the SVM parameters are optimized by using GA (genetic algorithm) and PSO (</span><span style="font-size: 10.5pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">particle swarm optimization) respectively. From experiment results, it can be concluded that both approaches, especially PSO-SVM, can solve the problem of estimating the optimal SVM parameter settings at a reasonable computational cost. Further, we point out the importance of a proper population size for GA/PSO-SVM, and present the recommended population size for GA-SVM and PSO-SVM.</span>

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In order to present a new method for analyzing the reliability of a two-link flexible robot manipulator, Lagrange dynamics differential equations of the two-link flexible robot manipulator were established by using the integrated modal method and the multi-body system dynamics method. By using the Monte Carlo method, the random sample values of the dynamic parameters were obtained and Lagrange dynamics differential equations were solved for each random sample value which revealed their displacement, speed and acceleration. On this basis, dynamic stresses and deformations were obtained. By taking the maximum values of the stresses and the deformations as output responses and the random sample values of dynamic parameters as input quantities, extremum response surface functions were established. A number of random samples were then obtained by using the Monte Carlo method and then the reliability was analyzed by using the extremum response surface method. The results show that the extremum response surface method is an efficient and fast reliability analysis method with high-accuracy for the two-link flexible robot manipulator.