Microwave filter assistant debugging method based on nuclear machine learning

Abstract

The invention discloses a microwave filter auxiliary debugging method based on kernel machine study, which mainly solves the problem that the prior art that does not construct a relationship model between the bolt adjustment amount and variable quantities of a coupling matrix. The method comprises the following steps: extracting the coupling matrix and processing data according to parameters of afilter S in engineering measurement to obtain normalized data sample sets of the bolt adjustment amount and the variable quantities of the coupling matrix; constructing the model of the influences ofthe bolt adjustment amount on the variable quantities of the coupling matrix by using the kernel machine study algorithm according to the sample sets; constructing an optimal adjustment model of the bolt adjustment amount of the filter according to the study model of the machine; and solving the optimal adjustment model to obtain the adjustment amount of each adjusting bolt of the filter. The method can rapidly and accurately carry out auxiliary debugging of the filter, and can be used for auxiliary debugging of mass-produced filters.

Description

technical field [0001] The invention belongs to the technical field of microwave filters, in particular to an auxiliary debugging method for microwave filters based on nuclear machine learning, which is used for guiding or assisting debugging of microwave filters. Background technique [0002] Microwave filters are widely used in communication systems. In actual production, due to the influence of machining accuracy and assembly errors, filter debugging is indispensable. However, because the influence of the adjustment bolts of the filter on the electrical performance is very complicated, it is difficult to debug. At present, all projects rely on manual experience for debugging. The debugging process is time-consuming and laborious, and requires rich experience of debugging personnel; for new debugging personnel, it is difficult to be competent for such a job. If the filter is to be produced in large quantities, it is usually necessary to employ many experienced debuggers,...

AI Technical Summary

Problems solved by technology

[0008] The filter auxiliary debugging method proposed in the above literature has the following defects: 1) extract the coupling matrix from the measured S parameters of the filter, and compare it with the designed coupling matrix. This method can only give the difference between the two, and cannot directly The adjustment amount of the filter bolt is given, which cannot be applied in actual engineering

2) The sensitivity of the adjustment amount to the influence of the coupling matrix variation adopts a linearization assumption, which does not conform to the actual situation, which limits the debugging effect

3) The adjustment method based on fuzzy logic and pattern recognition requires more fuzzy rule bases and data samples, as well as the difficulty of accurate extraction of characteristic parameters and acquisition of expert knowledge

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