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

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.

veri=xlsread('data200.xlsx');
input=veri(:,1:88);
target=veri(:,89:90);

input=pca(input);

x=input';
t=target';

% 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
view(net)

% 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)