Research On Key Issues Of FSV Method Used For Credibility Assessment Of Electromagnetic Simulation Result

Quantifying the credibility of simulation result can eliminate the uncertainty and instability introduced by subjective visual assessment, which is the core content of validity assessment of electromagnetic simulation model. Further, quantitative assessment can provide a range of useful information for the rectification and improvement of the simulation model. The Feature Selective Validation (FSV) method is the core of the electromagnetic simulation validity assessment system established by the IEEE standard1597.1. So it is important to study the properties, validity, functional extension and other key issues of FSV method, which will also meet the requirement of IEEE Standards Association on the revision and promotion of IEEE standard1597.1.Properties of FSV method are first investigated in this project. The applicability of FSV method in the credibility assessment of electromagnetic simulation result is demonstrated by comparing with some frequently used data comparison algorithms. The influence of the quantity of data points on the assessment result is evaluated by data sensitivity analysis. It is demonstrated that the FSV result keeps robust when data density and integrity of data sets are changed to some extent. The parameter sensitivity analysis examines the influence of the changing values of key parameters on the FSV results. In this way, two sensitive parameters are identified and their values are proved to be reasonable.Based on the comprehensive discussion of the properties of FSV method, the improvement in the representation of FSV qualitative result is proposed, which aims to reduce the discrepancy between FSV result and experts’ assessment. First, the histogram result is replaced by the probability density function which can give the qualitative results in detail and makes it possible to do cross statistical analysis between multiple data. Further, fuzzification of the correspondence relationship between FSV qualitative and quantitative results is studied, which is realized by introducing floating boundaries of qualitative categories. It is shown that the agreement between FSV prediction and experts’ assessment can be improved by the floating boundaries. In addition, characteristics of visual assessment are also analyzed by performing a new data credibility survey. These visual assessments offer reference information for the following research on the improvement of FSV.Assessment failures of FSV method in three situations are also discussed and improved. First, the problem occured in the assessment of data oscillating between positive and negative half planes is solved by improving the algorithm of Offset Difference Measure (ODM). Data tests suggest that this modification can reduce the disagreement between FSV results and visual assessment without any pre-processing to original data sets. Secondly, the MAF-FSV method is proposed to improve the assessment of transient data sets, which divides the data sets under comparison into three regions and modifies the algorithm of Feature Difference Measure (FDM). Additionally, the assessment of distorted data sets is discussed. New Global Difference Measure (GDM) is calculated after the data sets are corrected by the Feature Selective Correction (FSC) technique. All of these improvements are validated by comparing with the survey results.To assess the credibility of multiple simulation results, the comprehensive assessment method is established based on the FSV method, fuzzy comprehensive assessment model and the Analytic Hierarchy Process (AHP), which extends the function of FSV method. Then the method is applied to the validation of the reduction model of multiconductor crosstalk problem in high frequency. In this process, improvements of FSV proposed in this project are also used. The validity and advantages of the proposed model are demonstrated by the comprehensive credibility assessments of simulation results in frequency and time domain.