MATLAB: I have the following code while running i’m getting parse error at ‘clear’:usage might be invalid MATLAB syntax. I’m unable to fix this error could anyone help me..?

Question

function nmse_calc = nmse(wbr, net, input, target)
% wbr contains the weights and biases vector in row vector form as 
% passed to it by the genetic algorithm. This must be transposed 
% when being set as the weights and biases vector for the network.
% net must be configured to the sizes of input and target
% input I x N matrix of N I-dimensional "I"nput vectors
% target O x N matrix of N O-dimensional "O"utput target vectors
%  vart1 = mean(var(target',1)); % Reference MSE
 net = setwb( net, wbr' );
output = net( input );
nmse_calc = mean( ( target(:) - output(:) ).^2 ) / vart1;
 end
clear all,
close all,
clc, 
plt=0;
tic
    [ x t ] = simplefit_dataset;
    [ I N ] = size(x);%[ 1 94 ]

    [O N ] = size(t);%[ 1 94 ]
% Reference MSE: Average Target Variance

    vart1 = mean(var(t',1)) ;% 8.3378 

% No. of Training Equations

    Ntrneq = N*O; % 94 

% No. of unknown weights for H hidden Nodes

% Nw = ( I +1 )*H + (H +1)*O

% No Overfitting Upper Bound for H

% Nw < Ntrneq <==> H < Hnoub

    Hnoub = ( Ntrneq - O ) / ( I + O + 1 ); % 31

% Simplefit Data Plot: Target vs Input

    plt = plt+1;
    figure( plt ), plot( x, t ), 
% No. of Smooth Plot Local Extrema

    NLE = 4;
% Hidden Node Bounds

% NLE <= H < Hnoub % 4 <= H <= 31

% Hidden Node Choice

    H = NLE;
    Nw = ( I + 1)*H + ( H +1)*O; % 4, 13

% Create Regression/Curve-Fitting Neural Network:

              net = fitnet( H );
             net.divideFcn = 'dividetrain'; % Nval = Ntst = 0

% Configure the Net for the Simplefit Dataset

               s = rng( 'default' );
               net = configure( net, x, t );
% Initial Weights and Normalized MSE 
               wbr = getwb( net )'; % row vector
               NMSE0 = mse( t - net( x ) ) / vart1;% 2.7851
% Create handle to the NMSE function, 
               hand = @(wbr) nmse( wbr, net, x, t );
% Set the Genetic Algorithm tolerance for minimum change in fitness function % before terminating algorithm to 1e-4 and display each iteration's results.
               gaopts = gaoptimset( 'TolFun', 1e-4, 'display', 'iter' );
              [ wbopt, errga ] = ga( hand, Nw, gaopts );
% Optimization terminated: average change in the fitness value less than 
% options.TolFun.
% Best Mean Stall
% Generation f-count f(x) f(x) Generations
% 146 29400 0.1345 1777 29
              errga = errga; % 0.13448

              wbopt = wbopt;
% wbopt = [13.532 -7.440 -24.08 -8.759 12.595 5.777 -21.553 
% 20.281 4.983 2.095 8.649 -1.585 -0.522 ]
        net = setwb( net, wbopt');
        NMSE = mse( t - net( x ) ) / vart1; % 0.13448
        totaltime = toc; % 690 sec (11.5 min)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% FOR COMPARISON 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all, close all;
clc, plt = 0; tic
    [ x t ] = simplefit_dataset;
    [ I N ] = size(x); % [ 1 94 ]

    [O N ] = size(t); % [ 1 94 ]
% Reference MSE: Average Target Variance
    vart1 = mean(var(t',1)); % 8.3378 
% No. of Training Equations
    Ntrneq = N*O; % 94 
% No. of unknown weights for H hidden Nodes
% Nw = ( I +1 )*H + (H +1)*O
% No Overfitting Upper Bound for H
% Nw < Ntrneq <==> H < Hnoub
    Hnoub = ( Ntrneq - O ) /( I + O + 1 ); % 31
% Simplefit Data Plot: Target vs Input
    plt = plt+1;
    figure( plt ), plot( x, t ), 
% No. of Smooth Plot Local Extrema
    NLE = 4 ;
% Hidden Node Bounds
% NLE <= H < Hnoub % 4 <= H <= 31
% Hidden Node Choice
    H = NLE;
    Nw = ( I + 1)*H + ( H +1)*O; % 4, 13
% Create Regression/Curve-Fitting Neural Network:
    net = fitnet( H );
    net.divideFcn = 'dividetrain'; % Nval = Ntst = 0
% Configure the Net for the Simplefit Dataset
     s = rng( 'default' );
     net = configure( net, x, t ); 
     net = train(net,x,t);
     NMSE = mse( t - net( x ) ) / vart1; % 3.274e-4
     totaltime = toc; % 11 sec

Answer 1

You did not copy the original code correctly:

http://www.mathworks.com/matlabcentral/newsreader/view_thread/344888#944621

1. The function nmse should be stored in a separate file

2. You divided the first line into separate lines ending in commas

Hope this helps.

Greg