Tuning Model For Microwave Filter By Using Improved Back-Propagation Neural Network Based On Gauss Kernel Clustering

Given the difficulty of a single model in dealing with complex systems. In this study, we propose a tuning model that uses a probabilistic fusion of sub-optimal back-propagation neural network based on the Gauss kernel clustering. This study focused mainly three aspects of work compared with the traditional tuning model. First, the calculation of the coupling matrix of scattering parameters is achieved by solving polynomial coefficients after eliminating the inconsistent phase shift and resonant cavity loss. Second, the best clustering center and a number were obtained by mapping the scattered data to high-dimensional space, and the prediction of multi-output variables were realized by sub-model probability fusion. Third, an improved shuffled frog leaping algorithm was introduced to optimize the initial weights of the back-propagation neural network, and a differential operation significantly improved the diversity of the population and the searchability of the algorithm. Finally, the experiment of nine-order cross-coupled filters shows that the proposed method has a better capability to train the weights and thresholds, which improves the generalization performance of the system.