没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
摘要
I
摘 要
目前,微波滤波器在民用和军用射频领域有着广泛应用。针对当前无线通信
技术快速发展的趋势,需要在微波设计仿真中既快速又精确完成电路的仿真。因
此,建立一套高效率、高精度的滤波器设计方法十分必要。本文主要以宽带滤波
器为研究对象,运用机器学习模型建构适合的电磁代替模型,来做为微波滤波器
计算机辅助设计方法,进而提高宽带滤波器尺寸结构设计的效率和精度。
本论文首先简述了滤波器的设计理论,主要有三个部分:(
1
)系统函数的
获得。(2)滤波器的实现。(3)滤波器的仿真与优化。将在四种含平行耦合线
结构设计理论分析的基础上,给出滤波器智能化设计方法的总体框架。
其次为缩短滤波器的设计周期,提出通过电磁仿真 ADS(Advanced Design
System)
和
MATLAB
的联合编程,自动化批量生成数据以供网络模型训练, 这对
数据驱动的优化算法至关重要。网络模型作为数据驱动的优化算法,需要大量的
训练数据来提升性能。该方法直接建立了宽带滤波器的特征响应与物理尺寸参数
之间的内在联系,无需深入的理论推理或求解非线性方程,有效提高了宽带滤波
器设计的效率和实用性。
再者提出了基于 K-近邻回归算法的单级滤波器设计。该设计方法以联合仿
真采集的单级宽带滤波器的特性响应曲线作为输入,将特性响应曲线对应的物理
尺寸作为输出变量进行网络建模,能准确、高效地实现与传统理论分析方法下相
同的宽带滤波器设计功能。同时,已经训练好的网络结构可以用来实现在数据采
集范围内不同带宽的设计指标下的单级宽带滤波器设计。
最后,本文提出了对比了
BP
神经网络和
AutoML
自动机器学习的两级宽带
滤波器的设计结果。挑选出结果较好的一个网络模型再进行进一步的分析训练。
实验结果表明,使用自动机器学习方法可以显著提高宽带滤波器的设计效率和准
确性,并且相比于传统的滤波器设计,自动机器学习方法可以更快速的在误差范
围内找到想要的滤波器设计物理尺寸。通过大量实验数据和验证,证明了该方法
在实际设计中具有广泛的适用性和实用价值。未来,该方法还可以进一步改进,
包括使用更多的数据集和优化算法来提高模型的准确性和鲁棒性,以及将该方法
应用于其他微波器件的设计。
关键词:宽带滤波器;联合仿真;ADS;自动化;机器学习
Abstract
II
Abstract
Currently, microwave filters are widely used in civil and military RF fields. In
view of the current trend of rapid development of wireless communication technology,
it is necessary to quickly and accurately complete circuit simulation in microwave
design simulation. Therefore, it is necessary to establish a set of efficient and
high-precision filter design methods. This paper mainly focuses on broadband filters,
using machine learning and deep learning to construct suitable electromagnetic
substitution models as a computer-aided design method for microwave filters, thereby
improving the efficiency and accuracy of the size and structure design of broadband
filters.
In this paper, the design theory of filter is briefly introduced firstly. There are
three parts: (1) Obtaining the system function. (2) The implementation of filter. (3)
Simulation and optimization of filter. Based on the theoretical analysis of four kinds
of structure design with parallel coupling lines, the general framework of intelligent
filter design method is given.
Secondly, in order to shorten the Design cycle of the filter, it is proposed that the
joint programming of electromagnetic simulation ADS(Advanced Design System) and
MATLAB can automatically generate batch data for network model training, which is
very important for data-driven optimization algorithm. As a data-driven optimization
algorithm, network model needs a lot of training data to improve its performance.
This method directly establishes the internal relation between the characteristic
response of wideband filter and the physical dimension parameters, without in-depth
theoretical reasoning or solving nonlinear equations, and effectively improves the
efficiency and practicability of wideband filter design.
Third,The design of single stage filter based on K- nearest neighbor regression
algorithm is presented. This design method takes the characteristic response curve of
the single stage wideband filter collected by the co-simulation as the input and the
physical size corresponding to the characteristic response curve as the output variable
for network modeling. It can accurately and efficiently realize the same design
function of wideband filter as the traditional theoretical analysis method. At the same
time, the trained network structure can be used to realize the design of single-stage
wideband filter under different design indexes of bandwidth in the data acquisition
range.
III
Finally, a comparison between BP neural network and AutoML machine learning
is presented to design a two-stage wideband filter. Select a network model with good
results and then conduct further analysis training. The experimental results show that
the design efficiency and accuracy of wideband filter can be significantly improved
by using automatic machine learning method, and compared with traditional filter
design, automatic machine learning method can find the desired physical size of filter
design within the error range more quickly. Through a large number of experimental
data and verification, it is proved that the method has wide applicability and practical
value in practical design. In the future, the method can be further improved, including
using more data sets and optimization algorithms to improve the accuracy and
robustness of the model, as well as applying the method to the design of other
microwave devices.
Key Words:Broadband filter;co-simulation; ADS; robotization;machine learning
Abstract
目 录
1 绪论 ......................................................................................................... 1
1.1 研究背景及意义 ............................................................................... 1
1.2 国内外研究现状 ............................................................................... 2
1.3 本论文的主要研究内容................................................................... 4
2 设计理论分析 ........................................................................................ 6
2.1 传统平行耦合线带通滤波器设计理论 .......................................... 6
2.2 宽带平行耦合线带通滤波器设计理论 .......................................... 9
2.2.1 宽带滤波器理想系统函数的构造 ........................................... 10
2.2.2 不含传输零点的宽带滤波器设计 ........................................... 11
2.2.3 含传输零点的宽带滤波器设计 ............................................... 18
2.3 宽带滤波器智能化设计方法总体框架 ........................................ 21
2.4 本章小结 ......................................................................................... 22
3 滤波器响应数据的智能化采集 .......................................................... 23
3.1 MATLAB-ADS 集成程序 .............................................................. 23
3.2 样本基础的迭代解决方案 ............................................................ 24
3.3 数据采集 ......................................................................................... 25
3.4 数据预处理 ..................................................................................... 27
3.5 本章小节 ......................................................................................... 28
4 基于 K-近邻回归算法的单级宽带滤波器设计 ................................. 29
4.1 设计方法总体框架 ......................................................................... 30
4.2 基于 K-近邻回归的网络构建 ....................................................... 31
4.3 设计结果分析 ................................................................................. 35
4.3.1 相同带宽不同数据样本的结果分析....................................... 35
4.3.2 相同样本下不同相对带宽的结果分析 .................................. 37
4.4 本章小结 ......................................................................................... 39
5 两级宽带滤波器的智能化设计方法 .................................................. 40
5.1 设计方法总框架 ............................................................................. 42
5.2 模型的选择和训练 ......................................................................... 43
5.2.1 BP 神经网络 .............................................................................. 43
5.2.2 Auto ML 自动机器学习 ........................................................... 46
5.3 数据样本的获取及分析................................................................. 48
5.4 设计结果分析 ................................................................................. 49
5.4.1 相同数据样本下不同网络结构的结果分析 .......................... 49
5.4.2 相同网络结构的改变相对带宽结果分析 .............................. 50
5.5 本章小结 ......................................................................................... 51
6.总结和展望............................................................................................ 53
6.1 工作总结 ......................................................................................... 53
6.2 工作展望 ......................................................................................... 54
参考文献 ................................................................................................... 55
剩余66页未读,继续阅读
资源评论
icwx_7550592
- 粉丝: 17
- 资源: 7163
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功