Artificial neural network algorithm for automated filter tuning with improved efficiency by usage of many golden filters

This paper shows how to improve the efficiency of the Artificial Neural Network (ANN) method for the cavity filter tuning. It was proved that the usage of many golden filters in the process of collecting the learning vectors, used in ANN training, has the significant influence in decreasing the ANN generalization error. Thus, the algorithm efficiency is increasing. The generalization error value of ANN, trained on samples from two different filters, as a norm of the filters similarity is proposed. The tuning experiment for the 6-cavities RX filters of GSM diplexer has been demonstrated. In the experiment the ANNs were trained based on the vectors collected from up to five different filters, showing the significant influence of the number of “known filters” on the ANN generalization error.