采用快速空间映射算法多模超宽带滤波器有效设计方法

文章用快速空间映射算法（ASM）来优化和设计多模（MMR）超宽带滤波器。快速空间映射算法需要一个粗糙物理模型以及一个精确物理模型。文中用MATLAB来建立粗糙物理模型,使用商业仿真软件HFSS10来建立精确物理模型。快速空间映射算法就是用来找到粗糙模型和精确模型之间的映射关系,从而达到快速和精准设计超宽带滤波器的目的。     

空间映射粒子群算法用于电磁优化计算

将粒子群算法与空间映射算法相结合,提出了一种空间映射粒子群优化算法,并用于电磁问题的优化计算.算法实现过程中,将电磁仿真计算中精确网格剖分的计算模型作为精确模型,将粗糙网格剖分计算模型作为粗糙模型,在粒子群算法中计算粒子适应度前,使用粗糙模型结合基于卡尔曼滤波的映射关系,估计出粒子是否对算法最优解更新有效,并对有效的部分粒子做适应度计算,可以很大程度上减少算法的计算耗时.通过E型贴片天线和谐振腔缝隙天线的优化仿真说明了本算法的有效性.     

多目标神经空间映射算法优化设计微带滤波器

提出了一种多目标形式的空间映射算法,并将其应用于微带滤波器的优化设计问题.该算法没有采用ADS仿真软件进行粗糙模型的计算,而是采用多目标粒子群算法结合矩阵数值计算的形式,优化计算微带线的S参数,并应用神经网络映射粗糙模型与精确模型的仿真结果,由粗糙模型和神经网络推测出精确模型的帕累托最优解集.     

新型高收敛隐式空间映射算法设计微波滤波器

该文介绍了一种高收敛隐式空间映射算法,改进了隐式空间映射算法中粗糙模型到精细模型之间参数映射。它加入粗糙模型参数选择过程,避免了粗糙模型优化中的假收敛情况,加快精细模型与设计目标的逼近速度。参数选择过程不需要增加精细模型的计算次数,提高了优化效率。该文通过设计一个发卡形滤波器,并与以前的隐式空间映射算法计算结果进行比较,优化得到了比指标性能更优越的结果。同时证明了新算法比旧算法具有更快的逼近速度和更高的优化效率的优点。     

改进的隐式空间映射算法的研究

介绍了一种快速收敛空间映射算法,改进了隐式空间映射算法中粗糙模型到精细模型之间参数映射。通过增加限定参数提取的方式,减少粗糙模型的参数空间而实现粗糙模型响应高效准确逼近精细模型响应。通过设计一个交叉耦合滤波器,与之前的隐式空间算法进行比较,更容易达到优化目标,证明了限定参数提取算法具有更快的逼近速度和更高的优化效率的优点。     

空间映射算法优化八木天线

天线的最大增益是通过天线合理布局降低它们之间的干扰来实现的.工程中大多采用简单的数学模型并结合测试方法来进行天线布局设计的,但是这种方法效率低且不够精确.运用空间映射算法优化天线阵的位王参量,可以大大提高运算速率,节省时间.根据空间映射算法理论,提出并建立两种模型:一种是运算速度快,但不够精确的粗糙模型,另一种是运算速度较慢,但比较精确的精细模型.通过在这两种模型中建立映射,并利用遗传算法优化粗模型,兼顾精度和速度,从而得到最优解,为实际工程提供理论上的依据.     

渐进空间映射算法优化设计柱面矩形贴片天线

将空间映射算法推广到共形天线的优化设计之中,并设计了一个谐振频率在2.5 GHz的柱面矩形贴片天线.粗糙模型和精细模型分别采用腔模理论和全波电磁仿真软件HFSS进行分析,并通过渐进空间映射算法联系起来.计算结果表明,与直接优化精细模型相比,该方法可以大大减少计算时间,提高设计效率.     

交指型MEMS滤波器的优化设计

交指型微机电系统(MEMS)滤波器是采用MEMS工艺制备出的交指型结构滤波器,针对交指型MEMS滤波器电磁优化算法中存在速度慢,占用计算资源大等问题,对交指型MEMS滤波器优化设计方法进行了研究。采用主动空间映射算法,在ADS和HFSS软件中建立滤波器电路结构模型和三维结构电磁模型,并分别将它们用于主动空间映射算法所需的粗糙仿真和精确仿真,仅进行了5次精确仿真就完成了一款Ku波段滤波器的优化设计。结果表明,设计的滤波器中心频率为14 GHz,顶部损耗为1.6 dB,-1 dB带宽为2.2 GHz,满足设计指标要求,同时验证了优化设计方法的可行性。     

基于隐式空间映射算法的双频带滤波器设计

将差分进化算法用于隐式空间映射(ISM)的参数提取中,并对差分进化算法中的种群更新和交叉概率因子进行了改进,可以有效解决参数提取过程中算法的假收敛及收敛速度过慢的问题,改进了隐式空间映射算法中粗糙模型(CM)与精细模型(FM)之间的参数映射的相关性,迭代次数明显减少。通过设计一个微带双频带滤波器对该算法进行了验证,该双频带滤波器的中心频率分别为2.45GHz和5.25GHz,其粗糙模型和精确模型分别在Agilent ADS和Ansoft HFSS中进行计算分析。由结果可知,在精细模型中的仿真次数明显减少,有效地提高了滤波器的设计效率。     

基于空间映射算法的微带滤波器优化设计

考虑到传统电磁优化仿真方法的低效性,在原始空间映射算法的基础上,构造一种有效的输入一输出空间映射算法,并用于优化设计一个微带带通滤波器,其替代模型和精确模型分别采用了基于电路原理的AgilentADS和基于矩量法的全波电磁仿真软件FEKO进行计算分析。仿真结果表明,该算法能够有效增加所设计器件的优化自由度,大大减少计算机优化时间,有效提高了微波器件的设计效率。     

Efficient Design Centering of High-Frequency Integrated Continuous-Time Filters

We apply ldquospace-mappingrdquo optimization for design centering high-frequency integrated continuous-time filters. By appropriately choosing the coarse model, a significant simplification of the space mapping technique is possible, since the coarse model design space is almost a translated version of the fine model design space. Graphical intuition is given for the simplified technique. A design example of a fifth-order 71.4-500-MHz constant-C scaled G_m-C Chebyshev ladder filter is presented. Measurement results from fabricated ICs are given.     

An explicit knowledge-embedded space mapping technique and its application to optimization of LTCC RF passive circuits

A novel explicit knowledge embedded space mapping (SM) optimization technique is presented in this paper. It generalizes the implementation procedure of the efficient SM technique by introducing a buffer space called embedded knowledge space between the original coarse model space and the fine model space, where the ingredients of the coarse model space can be completely different from those of the fine model space. Therefore, this generic scheme can be used to map the coarse model space of arbitrary physical content to the fine model space of different physical content through the embedded knowledge space that is built up with the available radio frequency (RF) circuit CAD formula. The emphasis of the application of the proposed scheme is put on the design of low temperature cofired ceramic (LTCC) RF passive circuits in this paper, along with the required CAD formulas (knowledge) for typical embedded multilayer passives. The effectiveness of the proposed new scheme is demonstrated through two design examples of LTCC lumped element band pass filters for wireless applications. The detailed procedure and flowchart of the proposed implementation scheme are also given in the paper.     

A Space-Mapping Framework for Engineering Optimization—Theory and Implementation

This paper presents a comprehensive approach to engineering design optimization exploiting space mapping (SM). The algorithms employ input SM and a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest. Output SM ensures the matching of responses and first-order derivatives between the mapped coarse model and the fine model at the current iteration point in the optimization process. We provide theoretical results that show the importance of the explicit use of sensitivity information to the convergence properties of our family of algorithms. Our algorithm is demonstrated on the optimization of a microstrip bandpass filter, a bandpass filter with double-coupled resonators, and a seven-section impedance transformer. We describe the novel user-oriented software package SMF that implements the new family of SM optimization algorithms.     

Enhancing the Convergence Speed of Space Mapping Technique in Cylinder Conformal Antennas Optimization

Space mapping (SM) technique is applied in conformal antenna design. The main idea of this technique is to map the coarse model of a planar layer structure microstip antenna to fine structure of a conformal antenna including platform through space mapping. This technique is very suitable for conformal antenna optimization where long computational time is required to achieve an accurate solution. As the convergence speed of this technique is related to the response functions, some response functions are researching for accelerating the optimization. Two examples are studied to validate the advantages of the feasible response functions for accelerating the convergence speed of SM technique.     

Space-mapping optimization of microwave circuits exploitingsurrogate models

A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a novel way, a linear frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at different sets of frequencies. This approach is shown to be especially powerful if a significant response shift exists. The algorithm is illustrated through the design of a capacitively loaded 10:1 impedance transformer and a double-folded stub filter. A high-temperature superconducting filter is also designed using decoupled frequency and SMs     

Accelerated Microwave Design Optimization With Tuning Space Mapping

We introduce a tuning space-mapping technology for microwave design optimization. The general tuning space-mapping algorithm is formulated, which is based on a so-called tuning model, as well as on a calibration process that translates the adjustment of the tuning model parameters into relevant updates of the design variables. The tuning model is developed in a fast circuit-theory based simulator and typically includes the fine model data at the current design in the form of the properly formatted scattering parameter values. It also contains a set of tuning parameters, which are used to optimize the model so that it satisfies the design specification. The calibration process may involve analytical formulas that establish the dependence of the design variables on the tuning parameters. If the formulas are not known, the calibration process can be performed using an auxiliary space-mapping surrogate model. Although the tuning space mapping can be considered to be a specialized case of the standard space-mapping approach, it can offer even better performance because it enables engineers to exploit their experience within the context of efficient space mapping. Our approach is demonstrated using several microwave design optimization problems.      

EM-based optimization exploiting partial space mapping and exact sensitivities

We present a family of robust techniques for exploiting sensitivities in electromagnetic (EM)-based circuit optimization through space mapping (SM) technology. We utilize derivative information for parameter extractions and mapping updates. We exploit a partial SM (PSM) concept, where a reduced set of parameters is sufficient for parameter extraction optimization. It reflects the idea of tuning and execution time is reduced. Upfront gradients of both EM (fine) model and coarse surrogates can initialize possible mapping approximations. We introduce several effective approaches for updating the mapping during the optimization iterations. Examples include the classical Rosenbrock function, modified to illustrate the approach, a two-section transmission-line 10:1 impedance transformer and a microstrip bandstop filter with open stubs.