How can i decrease MSE in my ANN model?

I am trying to do a predictive modelling using Neural Net time series for which i have 6 different input and 1 target which consist of 74 data i tried running using the app provided(used NARX) but the MSE levels are very high . the code mention below is the one generated , can i decrease my MSE levels by chnaging number of neurons , delay ?

% Solve an Autoregression Problem with External Input with a NARX Neural Network

% Script generated by Neural Time Series app

% Created 07-Mar-2019 15:30:41

%

% This script assumes these variables are defined:

%

% input - input time series.

% taget - feedback time series.

X = tonndata(input,true,false);

T = tonndata(taget,true,false);

% 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 = 'trainlm'; % Levenberg-Marquardt backpropagation.

% Create a Nonlinear Autoregressive Network with External Input

inputDelays = 1:2;

feedbackDelays = 1:2;

hiddenLayerSize = 20;

net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);

% Prepare the Data for Training and Simulation

% The function PREPARETS prepares timeseries data for a particular network,

% shifting time by the minimum amount to fill input states and layer

% states. Using PREPARETS allows you to keep your original time series data

% unchanged, while easily customizing it for networks with differing

% numbers of delays, with open loop or closed loop feedback modes.

[x,xi,ai,t] = preparets(net,X,{},T);

% Setup Division of Data for Training, Validation, Testing

net.divideParam.trainRatio = 60/100;

net.divideParam.valRatio = 20/100;

net.divideParam.testRatio = 20/100;

% Train the Network

[net,tr] = train(net,x,t,xi,ai);

% Test the Network

y = net(x,xi,ai);

e = gsubtract(t,y);

performance = perform(net,t,y)

% View the Network

view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)

%figure, ploterrhist(e)

%figure, plotregression(t,y)

%figure, plotresponse(t,y)

%figure, ploterrcorr(e)

%figure, plotinerrcorr(x,e)

% Closed Loop Network

% Use this network to do multi-step prediction.

% The function CLOSELOOP replaces the feedback input with a direct

% connection from the outout layer.

netc = closeloop(net);

netc.name = [net.name ' - Closed Loop'];

view(netc)

[xc,xic,aic,tc] = preparets(netc,X,{},T);

yc = netc(xc,xic,aic);

closedLoopPerformance = perform(net,tc,yc)

% Step-Ahead Prediction Network

% For some applications it helps to get the prediction a timestep early.

% The original network returns predicted y(t+1) at the same time it is

% given y(t+1). For some applications such as decision making, it would

% help to have predicted y(t+1) once y(t) is available, but before the

% actual y(t+1) occurs. The network can be made to return its output a

% timestep early by removing one delay so that its minimal tap delay is now

% 0 instead of 1. The new network returns the same outputs as the original

% network, but outputs are shifted left one timestep.

nets = removedelay(net);

nets.name = [net.name ' - Predict One Step Ahead'];

view(nets)

[xs,xis,ais,ts] = preparets(nets,X,{},T);

ys = nets(xs,xis,ais);

stepAheadPerformance = perform(nets,ts,ys)

Thank you in advance