MATLAB: I want to Create a Cylinder with multiple layers and different dielectrics at each layer. Essentially I am trying to design a simple multilayer absorber around a cylinder from a monolayer to a multilayer one

multilayer absorbers

I am trying to create a multi-layer absorber around a cylinder. I intend creating a cylinder with a number of layers from 1 for a monolayer abosrber out to 10 for multi layers I am looking for information on this type of absorber design. The following is some code. Eps_r and Sig_r are values pre defined in an array

% code radius=5 numcyl=10; for j=ja:jb for i=ia:ib xdist=(ic-i); ydist=(jc-j); dist=sqrt((xdist^2)+(ydist^2)); if(dist<=radius) eps_r=epsilon(1); sig_r=sigma(1); elseif (dist<=radius+.05 eps_r=epsilon(2); sig_r=sigma(2); elseif (dist<=radius+.10) eps_r=epsilon(3); sig_r=sigma(3); endif end end end

Best Answer

Are you trying to create a vizualization or the actual material model?

For visualization you could use the CYLINDER command with different inputs to create cylinders. For modeling the material, you could write a function encapsulating the mathematical model of the material.

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MATLAB: Multilayer neural network with newff

If you are new with NNs stick with the defaults as much as possible. Input-Hidden-Output is sufficient for a universal approximator. It is very seldom that more is needed.

Although NEWFIT (regression) and NEWPR(classification) that call NEWFF (generic) are still available, they all are obsolete.

Do you have access to the current functions FITNET(regression) and PATTERNNET(classification) that call FEEDFORWARDNET (generic)? If so, choose one of the first two.

First practice on the MATLAB examples in the help and doc documentation.

First accept all defaults. If that doesn't work, increase the number of hidden nodes.

Don't hesitate to consult us again if you need help.

Hope this helps.

Greg

More examples can be obtained from

 help nndatasets
 doc nndatasets

Hope this helps.

Greg

MATLAB: Backpropogation ,Multilayer perceptron,neural network

% function TrainingNet

% load Feature.txt; %load the features

% FeatureS = Feature'; %Convert to column array

% load Outtype.txt; %load output type

% OuttypeS = Outtype';

[I N ] = size(FeatureS)

[O N ] = size(Outtypes)

minmaxF = minmax(FeatureS) % Is a matrix [I 2]

Neq = N*O % Number of training equations

% I-H-O node topology

% Nw = (I+1)*H+(H+1)*O % Number of unknown weights

% Want Neq >> Nw or % H << Hub

Hub = (Neq-O)/(I+O+1) % Neq = Nw

r = 10 % Neq > r*Nw, ~2 < r < ~30

H = floor((Neq/r-O)/(I+O+1))

How did you get H = 2000 ???

% %initialize parameters for creating the MLP.

% fcnCELL = {'logsig' 'logsig'};

% initflag = [0 1];

What does initflag do?

% trainalgo = 'gdm';

% paramatrix = [10000 50 0.9 0.6]; % epochs = 100, show = 50,

100 or 10,000?

                                   % learning rate = 0.9, momentum term = 0.6

% sameWEIGHT = [];

I suggest first using the defaults in NEWFF

% net_FFBP = creteNet(inputsize, mimax, hneurons, fcnCELL, initflag, trainalgo, paramatrix, sameWEIGHT);

Is this supposed to be a replacement for NEWFF and net.Param.* ??

% net_FFBP = newff(FeatureS, OuttypeS, 39);

Now H = 39 ??

% [net_FFBP] = train(net_FFBP, FeatureS, OuttypeS);

% save net_FFBP net_FFBP;

% disp('Done: Training Network');

What is your question ??

Greg

Best Answer

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MATLAB: Multilayer neural network with newff

MATLAB: Backpropogation ,Multilayer perceptron,neural network

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