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Astudy on Optimizations and Designs of RF Filter

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Tutor: BaoYaMing
School: Nanjing University of Posts and Telecommunications
Course: Electromagnetic Field and Microwave Technology
Keywords: Filter optimization,Team progress algorithm,Ultra-wideband filter,Objective func
CLC: TN713
Type: Master's thesis
Year:  2012
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Abstract:
In recent years, with the rapid development of electronic technology , the filter as an important part of the circuit , the performance requirements continue to increase , so that the filter the traditional theoretical design method has been difficult to meet . The RF filter optimization design usually by means of electromagnetic simulation software . Through a number of evolutionary algorithm combined electromagnetic simulation software for the optimization of the filter is the hot spot of the current filter to optimize the design of the study . This article uses a team progress (TPA) algorithm for the optimization algorithm , the use of plug - in optimizer ultra wideband filter with a particular multi-mode first carried out to the optimization of the echo loss in the band for the target , so that the filter passband return loss smaller. In order to improve the filter multiple performance indicators , the paper proposes a combination of objective function is applied to the plug-in optimizer , and the same ultra-wideband filter band return loss and band rejection for the combination of the objective function of optimization . Through repeated testing, to determine the value of the weighting coefficient in the composition in the objective function , to achieve the requirements of the combined objective function . In addition, the use of a genetic algorithm (GA) HFSS simulation software provides the ultra- broadband filter optimization of the same combination of target and compare it with the plug-in optimizer . In the case of a considerable amount of computation , GA algorithm failed to meet the objectives and requirements ; combination of plug- optimizer objective optimization not only get a better optimization results , and less time-consuming , plug optimizer combined objective function fully proved feasible and superiority .
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