Inverse Modeling in Application for Sequential Filter Tuning

Abstract

Abstract-This paper presents a new method of sequential microwave filter tuning.For filters with R tuning elements (including cavities, couplings and cross-couplings), based on physically measured scattering characteristics in the frequency domain, the Artificial Neural Network (ANN) is used to build inverse models of R sub-filters. Each sub-filter is associated to one tuning element. The sub-filters are obtained by successive opening or shorting of resonators and by removing coupling screws. For each sub-filter, the ANN training vectors are defined as physical reflection characteristics (input vectors) and the corresponding positions of the tuning element, which is detuned, in both directions, from its proper setting (output vectors). In the tuning process, such inverse models are used for calculating the tuning element increments needed for setting the tuning element in the proper position. The tuning experiment, conducted on 8-and 11-cavity filters, has shown the performance of the presented method.

Improved Extraction of Coupling Matrix and Unloaded Q From S-Parameters of Lossy Resonator Filters

“…A sequential tuning method was also reported in [4]. However, these methods in [1][2][3][4] have not taken the source-load coupling into account.…”

“…A sequential tuning method was also reported in [4]. However, these methods in [1][2][3][4] have not taken the source-load coupling into account. The methods in [1,2] require assuming all the resonators with the same unloaded Q.…”

Filter Tuning Based on Linear Decomposition of Scattering Characteristics

“…On the other hand it can deal with many types of filters including very complex ones as shown in a tuning experiment, Section 5. An important advantage is a reduction of time needed to prepare a model for new filter type compared to methods [4][5][6][7]. A comparison of mentioned methods is depicted in Figure 1.…”

“…This mapping is performed with the use of artificial neural network (ANN) multidimensional approximator [4,5], neurofuzzy system [6] or linear matrix operator [7]. Especially [4,5] methods based on ANN are very well examined and proved to be very fast and able to deal with any type of filter (high complexity, many crosscouplings).…”

“…Similarly as in methods [4][5][6][7], method [8] considers direct correspondence between filter characteristics and tuning elements. Elaboration [8] is motivated by the fact that in methods [4,6,7] an inconvenient process of controlled, random filter detuning must be done for each filter type to train, e.g., Artificial Neural Network.…”