How to reduce features using PCA and get good result on Pattern recognition ?

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I have data 200x90. (rowsx columns)
My input has 200x88 and my target has 200x2 .
My data number (200 ) is less according to 88 features to get good accuracy on my neural network.
It should be more data to get good accuracy but I can't increase my data .
So I want to use PCA to reduce 88 features.
I loaded data200 and shared codes .
When I apply pca I got that error:
% Error using network/train (line 340)
% Inputs and targets have different numbers of samples.
% Error in deneme (line 50)
% [net,tr] = train(net,x,t);
Because it decreases my rows also. I want to keep my rows number (200) and just decrease column numbers.
I couldn't manage how to apply PCA in proper way.
% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by Neural Pattern Recognition app
% Created 07-Feb-2021 15:50:44
% This script assumes these variables are defined:
% x - input data.
% t - target data.
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.
% Create a Pattern Recognition Network
hiddenLayerSize = [10];
net = patternnet(hiddenLayerSize, trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
% View the Network
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotconfusion(t,y)
%figure, plotroc(t,y)

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