matlab articles

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  Hi guys,   I have a rather fundamental question regarding the analysis of my data involving nonlinear fitting and I hope it is appropriate to post it here. For the sake of brevity I will not provide the whole code and only summarize the essential steps, but of course I can add any details you request.   I have some data which represents some response to a stimulus as a function of the distance to the stimulation site. The data shows, as to be expected, a decay in the response variable, which may be best approximated by a sigmoidal fit. So I applied the BOLTZMANN equation to the data and let MATLAB predict confidence bounds for new observations:     % Define model function (BOLTZMANN); f = @(beta0,conds)beta0(1) + ((beta0(2)-beta0(1)) ./ (1+exp((beta0(3) - conds) ./ beta0(4)))); % Find initialization parameters: resp50 = (max(resp) + min(resp))/2; x50 = 5000; %Educated guess inidat = [0,max(resp),resp50,x50]; % Estimate the fitted function: [beta,res,jac,covb] = nlinfit(conds',

  Hello everybody   I am new to neural networks, but I have studied the theory and everything is OK. Now I've come to the practical part. I am using the Neural Network toolbox in Matlab, and start using NARX where x(t) is Excel file (1 column and 3500 rows) and y(t) is also an Excel file (1 column and 3500 rows). I import these files and train the system, and get the figures and everything is OK. My question is:   After doing all of the above ,   how do I use this network to predict the value of y for a given x ?   Here is the resulting script file :   % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by NTSTOOL % Created Fri Oct 12 20:48:21 IST 2012 % % This script assumes these variables are defined: % % EMA - input time series. % Close - feedback time series. inputSeries = tonndata(EMA,false,false); targetSeries = tonndata(Close,false,false); % Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:2;

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