S parameter grouping vector fitting method, electronic equipment and storage medium
By clustering and grouping and vector fitting of the matrix element functions of S parameters, fitting the same type of matrix element functions using the same pole, and fitting different types of matrix element functions using different poles, the problems of long modeling time and large model scale in the existing technology are solved, and high-precision and low-scale S parameter modeling are achieved.
CN119720907APending Publication Date: 2025-03-28EMPYREAN TECH CO LTD
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Patent Information
- Application Number
- CN202411770453.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
In S parameter modeling, the modeling time is too long and the model scale is too large, making it difficult to meet the high-precision modeling requirements.
Method used
Clustering calculation is used to group each matrix element function of the S parameter. The matrix element function of the same group is vector fitted with the same poles, and the matrix element function of different groups is fitted with different poles, thereby improving the vector fitting accuracy and reducing the model size.
Benefits of technology
Through the grouped vector fitting method, the vector fitting accuracy is improved, the total number of poles is reduced, the scale of subsequent sub-circuits is reduced, and the modeling efficiency and accuracy are improved.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an S parameter grouping vector fitting method, electronic equipment and a storage medium, and the method comprises the following steps: constructing a vector representation of each matrix element of an S parameter, and obtaining a vector representation set of the matrix elements of the S parameter; performing clustering calculation on elements in the vector representation set by taking the matrix dimension of the S parameter as a clustering category number; grouping the matrix element functions of the S parameter according to a clustering calculation result; and performing vector fitting on each group of matrix element functions. According to the method, a clustering calculation mode is adopted, all matrix element functions of S parameters are grouped, the matrix elements in the same group are fitted with the same pole, the matrix element functions in different groups are fitted with different poles, and compared with a method that the same pole is used for all the S parameter matrix element functions, the method has the advantage that the method is more accurate. The vector fitting precision is improved; and the scale of the model is reduced.
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