update

readme.txt

@@ -2,11 +2,14 @@
 Welcome everyone to exchange and correct, if you feel helpful, thanks for star and fork.
 みんなを歓迎して交流と指摘、もし助けがあって、ありがとうございますをクリックしてスター.
 
-project 1:基于神经网络的带通滤波器优化设计
+project 1:基于深度学习的滤波器优化设计
 	version 1.0  start time ：2017.12.17-
 	
 project 2:双频微带天线设计与神经网络优化
 	version 1.0  start time : 2017.12.21-
 	
-project 3:Coursera-deep learning.ai 
-	version 1.0  start time : 2018.1.16-
\ No newline at end of file
+project 3:Coursera：deep learning深度学习 
+	version 1.0  start time : 2018.1.16-
+	
+project 3:宽频带范围螺旋电感DNN建模 
+	version 1.0  start time : 2018.5.8-
\ No newline at end of file

基于神经网络的带通滤波器优化设计/readme.txt → 基于深度学习的滤波器建模优化/readme.txt

-名称：基于神经网络的带通滤波器设计优化
+名称：基于深度学习的滤波器设计优化
 内容：
 	利用HFSS在32-36GHz的条件下，仿真了一个带通滤波器结构；
 	利用仿真的数据数据S1=0.01:0.04:0.21,S2=0.26:0.02:0.46,W1=0.01:0.04:0.21,Freq=32:36
@@ -9,5 +9,5 @@
 说明：
 	neuralnetwork.m  文件是matlab程序
 	训练集测试集数据 文件夹是HFSS导出的原始数据和matlab处理后的数据
-	基于神经网络的带通滤波器设计仿真源文件.aedt  文件是HFSS仿真文件
-	基于神经网络的带通滤波器设计优化演讲版.pptx  文件是此项目的说明ppt
\ No newline at end of file
+	基于深度学习的滤波器设计仿真源文件.aedt  文件是HFSS仿真文件
+	基于深度学习的滤波器设计优化说明.pptx    文件是此项目的说明ppt
\ No newline at end of file

宽频率范围螺旋电感DNN建模/model.m

+%DNN电感建模
+%Author:Liujiangfan 
+%Time  :2018.5
+%Github:https://github.com/LSayhi
+%Addres:SCHOOL OF ELECTRONIC ENGINEERING at BUPT,Beijing ,China
+
+
+%说明
+%输入数据依次为频率（标准化/60G），电感内径（/29.6um），电感线宽(/5.1um)
+%输出数据依次为S11模值，S21模值，S11相位（弧度制），S21相位（弧度制）
+%输入输出若不标准化则可能导致模型不收敛或收敛变慢
+
+%定义
+num_inputlayer=3;%输入参数个数
+num_outputlayer=4;%输出参数个数
+
+input_train = traininput(1:num_inputlayer,:);%读入训练集输入
+output_train= trainoutput(1:num_outputlayer,:);%读入训练集输出
+input_test  = testinput(1:num_inputlayer,:);%读入测试集输入
+output_test = testoutput(1:num_outputlayer,:);%读入测试集输出
+
+[inputn,inputps]=mapminmax(input_train);%归一化训练集输入
+[outputn,outputps]=mapminmax(output_train);%归一化训练集输出
+
+net=newff(inputn,outputn,[10,10]);
+%{'tansig','tansig','purelin'}，激活函数
+%设置隐藏层层数和每层神经元数目，输入输出层由数据确定。
+
+net.trainParam.epochs=10000;%最大迭代次数1000
+net.trainParam.lr=0.01;%学习率0.1
+net.trainParam.goal=0.000001;%目标误差率
+% net.divideParam.trainRatio = 60/100;  
+% net.divideParam.valRatio = 30/100;  
+% net.divideParam.testRatio = 10/100; 
+%net.divideFcn = '';
+net=train(net,inputn,outputn);%训练设置好的神经网络模型
+
+inputn_test=mapminmax('apply',input_test,inputps);
+%测试集输入归一化（按训练集的归一化参数来，不是测试集按自己数据归一化）
+an=sim(net,inputn_test);%将测试集输入导入模型net仿真
+BPoutput=mapminmax('reverse',an,outputps);
+%反归一化测试集输出（按训练集的参数反归一化）
+
+
+%画直角坐标图
+figure(1);
+plot(testinput(1,:),BPoutput([1,3],:),'.')%预测S11的幅值和相位
+hold on;
+plot(testinput(1,:),output_test([1,3],:),'-')%仿真的S11幅值和相位
+legend('预测S11mag','预测S11deg','仿真S11mag','仿真S11deg')
+title('Simulated S by HFSS and predicted S by neural network')
+
+figure(2);
+plot(testinput(1,:),BPoutput([2,4],:),'.')%预测S21的幅值和相位
+hold on;
+plot(testinput(1,:),output_test([2,4],:),'-')%仿真的S21幅值和相位
+legend('预测S21mag','预测S21deg','仿真S21mag','仿真S21deg')
+title('Simulated S by HFSS and predicted S by neural network')
+
+%画极坐标图
+figure(3);
+polar(BPoutput(1,:),BPoutput(3,:),'.');%S11仿真
+hold on;
+polar(output_test(1,:),output_test(3,:),'-');%S11预测
+legend('仿真S11','预测S11')
+title('Simulated S by HFSS and predicted S by neural network')
+
+figure(4);
+polar(BPoutput(2,:),BPoutput(4,:),'.');%S21仿真
+hold on;
+polar(output_test(2,:),output_test(4,:),'-');%S21预测
+legend('仿真S21','预测S21')
+title('Simulated S by HFSS and predicted S by neural network')

宽频率范围螺旋电感DNN建模/readme.txt

+名称：宽频带范围螺旋电感DNN建模
+内容：
+	利用HFSS在1-119GHz的条件下，抽样仿真了一个螺旋电感，利用仿真数据建立电感模型；
+	输入为频率（标准化/60GHz）、电感内径（标准化/29.6um）、电感线宽（标准化/5.1um）
+	输出为MAGS11,MAGS21,DEGS11（弧度制）,DEGS21（弧度制）
+说明：
+	model.m  文件是matlab程序
+	训练集测试集数据 文件夹是HFSS导出的原始数据和matlab预处理后的数据
\ No newline at end of file

宽频率范围螺旋电感DNN建模/训练集测试集数据（.mat）/test_S11ang.csv

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宽频率范围螺旋电感DNN建模/训练集测试集数据（.mat）/test_S11mag.csv

+"Freq [GHz]","mag(S(1,1)) []"
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宽频率范围螺旋电感DNN建模/训练集测试集数据（.mat）/test_S21ang.csv

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宽频率范围螺旋电感DNN建模/训练集测试集数据（.mat）/test_S21mag.csv

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