Performance Degradation Prediction of IF Conversion Circuit Based on DE-GWO-SVM Model

Abstract

Due to the complex structure, nonlinearity and tolerance of IF conversion circuit, the failure probability of IF conversion circuit is greatly increased. To solve the above problems, a differential evolution adaptive grey wolf algorithm (DE-GWO) was proposed to optimize the parameters of support vector machine model, and the model was applied to the fault diagnosis of IF conversion circuit for the first time. Firstly, the energy feature of the output signal of IF conversion circuit was extracted by wavelet packet decomposition, which effectively reduced the dimension of feature vector. Secondly, the improved grey wolf algorithm was used to optimize the parameters of SVM model and establish DE-GWO-SVM fault diagnosis model; Finally, taking the IF conversion circuit as an example, the fault diagnosis experiment was carried out and compared with other methods. Comparison results show that the fault diagnosis accuracy rate of this method reaches 97.33%. Compared with the traditional methods, this method can better improve the fault diagnosis rate and shorten the diagnosis time.

Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

“…The offspring and the parent in the intermediate population compete to produce the dominant population of the next generation. With a dominant population to enlarge the search scope of wolves, avoid GWO comes algorithm falls into local optimum, guarantee GWO comes algorithm evolution in the direction of the optimal solution 25 …”

“…With a dominant population to enlarge the search scope of wolves, avoid GWO comes algorithm falls into local optimum, guarantee GWO comes algorithm evolution in the direction of the optimal solution. 25 The three populations are randomly produced in a practical area in the first step of the procedure employing Equation (23). Let POP stand for the following population definition:…”