Improved weighted support vector regression algorithm for vulnerability assessment of electronic devices illuminated or injected by high power microwave

At present weighted support vector regression(WSVR) algorithms almost select a function of an important eigen quantity to calculate the weights, which leads to high errors in doing regression in high dimension eigen space. Aiming at this problem, a method of determining weights by the Euclidean distance in high dimension eigen space is presented, therefore an improved weighted support vector regression algorithm is built up and applied to the vulnerability assessment of electronic devices illuminated or injected by high power microwave(HPM). The simulation results show that our method is more accurate than the algorithms using fuzzy neural network(FNN), standard support vector regression and common weighted support vector regression. Because of the additional process of calculating weights, the presented methods efficiency is as high as that of the common WSVR algorithm but a little lower than that of the standard support vector regression algorithm and the FNN algorithm.