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首页> 外文期刊>International journal of RF and microwave computer-aided engineering >Tuning model for microwave filter by using improved back-propagation neural network based on gauss kernel clustering
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Tuning model for microwave filter by using improved back-propagation neural network based on gauss kernel clustering

机译：基于高斯内核聚类的改进的背传播神经网络调整微波滤波器调谐模型

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摘要

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.
机译：鉴于处理复杂系统的单一模型的难度。在这项研究中，我们提出了一种调整模型，该调谐模型基于高斯内核聚类使用子最优背部传播神经网络的概率融合。本研究主要集中于与传统调整模型相比的工作三个方面。首先，通过在消除不一致的相移和谐振腔损失之后求解多项式系数来实现散射参数的耦合矩阵的计算。其次，通过将散射数据映射到高维空间来获得最佳聚类中心和多个，并且通过子模型概率融合实现了多输出变量的预测。第三，引入了一种改进的洗机青蛙跳跃算法以优化背部传播神经网络的初始重量，差分操作显着提高了群体的分集和算法的可搜索性。最后，九阶交叉耦合过滤器的实验表明，该方法具有更好的培训权重和阈值的能力，这提高了系统的泛化性能。

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