added datastructure folder

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diff --git a/datastructure/matlab/OptMat.JPG b/datastructure/matlab/OptMat.JPG
new file mode 100644
index 0000000..0570774
--- /dev/null
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diff --git a/datastructure/matlab/StruMat.JPG b/datastructure/matlab/StruMat.JPG
new file mode 100644
index 0000000..bc4388a
--- /dev/null
+++ b/datastructure/matlab/StruMat.JPG
diff --git a/datastructure/matlab/call.m b/datastructure/matlab/call.m
new file mode 100644
index 0000000..9f5db2a
--- /dev/null
+++ b/datastructure/matlab/call.m
@@ -0,0 +1,6 @@
+  u = idinput(1024,'PRBS',[0 1/20],[-1 1])

+  a = [1 0.5];b = [0 2 3];

+  model = idpoly(a,b,'Ts',0.1)

+  y = sim(u,model) + rand(length(u),1)

+  plantData = iddata(y,u,0.1)

+  sys = armax(plantData,[2,2,1,1])
\ No newline at end of file
diff --git a/datastructure/matlab/datMat.JPG b/datastructure/matlab/datMat.JPG
new file mode 100644
index 0000000..96f646b
--- /dev/null
+++ b/datastructure/matlab/datMat.JPG
diff --git a/datastructure/matlab/datMat1.JPG b/datastructure/matlab/datMat1.JPG
new file mode 100644
index 0000000..6567a52
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new file mode 100644
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diff --git a/datastructure/matlab/matlab_struct.pdf b/datastructure/matlab/matlab_struct.pdf
new file mode 100644
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diff --git a/datastructure/matlab/matlab_struct.tex b/datastructure/matlab/matlab_struct.tex
new file mode 100644
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+

+\documentclass[a4paper,12pt]{report}

+\usepackage{graphicx}

+\usepackage{booktabs}

+\usepackage{amsmath}

+\usepackage{listings}

+\usepackage[stable]{footmisc}

+\title{MATLAB Model and Data Structures}

+\begin{document}

+	

+

+\maketitle

+\tableofcontents

+

+

+	

+\chapter{Calling Sequence}	

+

+		\lstinputlisting{call.m}

+	

+	

+\chapter{Model structure (idpoly)}

+	

+	

+	\section{Model structure output \footnote{\label{dj}This option is also available in GUI (called using 'ident')}}

+	

+

+	\begin{enumerate}

+		\item Type of model : discrete-time\footref{dj}

+		\item Type of estimation method 

+		\item Equation showing relation between polynomials  

+		\item Model representation (A,B,C polynomials in increasing power of $z^{-1}$)

+			

+		\item Sample time

+		

+		\item Parameterization : Polynomial orders, Number of free parameters

+		\item Hint for functions that can be used for extra information like uncertainties in polynomial values and their covariance 

+		\item Information about whether the model is constructed with fixed parameters or estimated from data

+		\item Information about the data used to estimate the model

+		\item Fit percentage, Final Prediction Error(FPE), Mean Squared Error(MSE)

+		\item Focus -  Error to be minimized ('prediction' or 'simulation')

+	

+			

+			

+			

+			

+			

+			\begin{figure}[tbp]

+				\centering

+				\includegraphics[width=0.8\textwidth]{modMat1.JPG}

+				\caption{Model Output}

+				\label{}

+			\end{figure}

+			

+		\end{enumerate}	

+			

+		\section{Model structure attributes}	

+			

+		\begin{enumerate}	

+			\item Polynomial coefficients vector

+			

+			\item Addition of integrators in noise channel \footref{dj}

+			

+			\item Variable('$z^{-1}$') used in polynomials

+			

+			\item Transport delays for each input/output pair using IODelay

+		

+			\item Following information about each polynomial :

+		\begin{enumerate}

+			

+			\item Parameter values

+			\item Upper and lower bounds on values

+			\item Property of each coefficient - Tunable or not

+			\item Scaling factor used to normalize the parameter value

+			\item Unit and label for each parameter

+		

+		\end{enumerate}

+		

+		\item Value of variance of noise channel

+		

+		\item Detailed report with following information :  

+		

+		\begin{enumerate}

+			\item Status : model is constructed or estimated

+			\item Estimation method used 

+			\item Handling of initial conditions during estimation, specified as one of the following values : 'zero', 'estimate', 'backcast', 'auto' \footref{dj}

+			\item Values of Mean Squared Error(MSE), Final Prediction Error(FPE), Fit Percentage, Raw Akaike's Information Criterion(AIC), Small sample-size corrected (AICc), Normalized AIC(nAIC), Bayesian information criteria(BIC), Loss function

+			\item Information about parameters (free parameters vector, covariance, and label) \footref{dj}

+			\item Following options are available in OptionsUsed structure :

+			

+			\begin{enumerate}

+				\item Initial condition

+				\item Specifying whether to display the estimation progress ('off' or 'on') \footref{dj}

+				\item Input and output offset

+				\item Controlling whether parameter covariance data is generated \footref{dj}

+				\item Regularized estimation of model parameters using a constant that determines the bias versus variance tradeoff \footref{dj}

+				\item Following search methods available for iterative parameter estimation :

+				\begin{enumerate}

+					\item Subspace Gauss-Newton least squares search

+					\item Adaptive subspace Gauss-Newton search

+					\item Levenberg-Marquardt least squares search

+					\item Steepest descent least squares search

+					\item Non-linear least squares solver

+					\item Constrained non-linear solvers

+				\end{enumerate}

+				

+				\item Following search options are available :

+				\begin{enumerate}

+					\item Tolerance

+					\item Maximum iterations

+					\item Step tolerance, function tolerance

+				\end{enumerate}

+				

+				\item Focus - Error to be minimized ('prediction' or 'simulation') \footref{dj} 

+				\item Following options for weighting filter are available 

+			\begin{enumerate}

+				\item Passbands — Specify a row vector or matrix containing frequency values that define desired passbands

+				\item SISO filter

+			\end{enumerate}

+				\item Controlling whether to enforce stability of estimated model (estimated model must be stable) \footref{dj}

+				\item Following advanced options are available :

+				\begin{enumerate}

+					\item Error threshold to specify when to adjust the weight of large errors from quadratic to linear

+					\item Max Size to specify maximum number of elements in a segment when input-output data is split into segments

+					\item AutoInitThreshold to specify when to automatically estimate the initial condition

+				\item	StabilityThreshold to specify thresholds for stability tests

+					

+	\end{enumerate}

+\end{enumerate}

+				

+			\item Controlling random number generation at the start of estimation

+			\item Information about data used for estimation - type of data, length, sample time, offset in input or output, input intersample behaviour 

+			\item Following termination conditions for the iterative search used for prediction error minimization :

+			\begin{enumerate}

+				\item Reason

+				\item Number of iterations performed

+				\item Infinity norm of the gradient search vector when the search algorithm terminates

+				\item Number of function call

+				\item Norm of the gradient search vector in the last iteration

+				\item Criterion improvement in the last iteration

+				\item Algorithm used by 'lsqnonlin' or 'fmincon' search method

+			\end{enumerate}

+				

+				

+				

+				

+				

+				

+				

+			\end{enumerate}

+		

+		

+		

+		

+		

+		

+		

+		

+		

+		

+		

+		

+		

+	

+		\item Input Delay \footref{dj}

+	\item Output Delay

+	\item Sample time

+	\item Time unit

+	\item Name of input data vector (If not specified, default name 'u1')

+	\item Input unit

+	\item Input channel groups to assign input channels of MIMO systems into groups and refer to each group by name

+	\item Name of output data vector (If not specified, default name 'y1')

+	\item Output unit

+	\item Output channel groups to assign output channels of MIMO systems into groups and refer to each group by name

+			

+	\item Notes in the form of string (An arbitrary field to store extra information)

+	

+	\item Any other user data/comments

+	

+	\item Any arbitrary name for model 

+	\item Sampling grid for model arrays, specified as a data structure

+		

+		

+		

+	

+		

+		

+			

+	\end{enumerate}

+			

+			

+			

+		

+		\begin{figure}[tbp]

+			\centering

+			\includegraphics[width=0.8\textwidth]{modMat2.JPG}

+			\caption{Model attributes}

+			\label{}

+		\end{figure}

+	

+		\begin{figure}[tbp]

+		\centering

+		\includegraphics[width=0.8\textwidth]{StruMat.JPG}

+		\caption{sys.Structure}

+		\label{}

+	\end{figure}

+	

+	

+	

+	\begin{figure}[tbp]

+		\centering

+		\includegraphics[width=0.8\textwidth]{repMat.JPG}

+		\caption{sys.Report}

+		\label{}

+	\end{figure}

+	

+				

+			\begin{figure}[tbp]

+				\centering

+				\includegraphics[width=0.8\textwidth]{OptMat.JPG}

+				\caption{sys.Report.OptionsUsed}

+				\label{}

+			\end{figure}

+			\chapter{Data structure (iddata)}

+			\section{Data structure output}

+		

+			 

+			\begin{enumerate}

+				\item Domain of data : Time or frequency

+				\item Number of samples

+			\item Sample time

+			\item Output vector name

+			\item Output unit 

+			\item Input vector name

+		\item Input unit

+			

+		\end{enumerate}

+	\section{Data structure attributes}

+			

+			

+			

+			\begin{enumerate}

+			

+			\item Domain of data : Time or frequency	

+			\item Any arbitrary name for data 

+				

+			\item Output data vector stored in OutputData

+			

+			\item Output data vector stored in y

+				

+				\item Name of output vector which is displayed on iddata call (If not specified, default names, {'y1';'y2';...})

+				

+				 \item Output unit

+				 	\item Input data vector stored in InputData

+				 

+				 \item Input data vector stored in u

+				 

+				 \item Name of input vector which is displayed on iddata call (If not specified, default names, {'u1';'u2';...})

+				 

+				 \item Input unit

+			

+				

+				\item Period of input data 

+			

+			\item Inter sample behaviour of input : 'zoh', 'foh', or 'bl', depending on whether data is piecewise constant, piecewise linear, or band limited

+				

+			\item Sample time

+				

+			\item Starting time instant stored in Tstart

+				

+				\item Time instants stored in a vector

+				

+			\item Time unit

+				

+			\item Name of experiment (iddata can store data of multiple experiments)

+				

+		\item Notes in the form of string (An arbitrary field to store extra information)

+				

+			\item Any other user data/comments

+			\end{enumerate}

+			

+			

+			\begin{figure}[tbp]

+				\centering

+				\includegraphics[width=0.8\textwidth]{datMat1.JPG}

+				\caption{Data Output}

+				\label{}

+			\end{figure}

+		

+		\begin{figure}[tbp]

+			\centering

+			\includegraphics[width=0.8\textwidth]{datMat.JPG}

+			\caption{Data attributes}

+			\label{}

+		\end{figure}

+			

+%\chapter{Data structure (iddata)}

+

+

+

+

+\end{document}

diff --git a/datastructure/matlab/matlab_struct.toc b/datastructure/matlab/matlab_struct.toc
new file mode 100644
index 0000000..71210e6
--- /dev/null
+++ b/datastructure/matlab/matlab_struct.toc
@@ -0,0 +1,7 @@
+\contentsline {chapter}{\numberline {1}Calling Sequence}{2}
+\contentsline {chapter}{\numberline {2}Model structure (idpoly)}{3}
+\contentsline {section}{\numberline {2.1}Model structure output \FN@sf@gobble@opt {This option is also available in GUI (called using 'ident')}}{3}
+\contentsline {section}{\numberline {2.2}Model structure attributes}{4}
+\contentsline {chapter}{\numberline {3}Data structure (iddata)}{11}
+\contentsline {section}{\numberline {3.1}Data structure output}{11}
+\contentsline {section}{\numberline {3.2}Data structure attributes}{11}
diff --git a/datastructure/matlab/modMat1.JPG b/datastructure/matlab/modMat1.JPG
new file mode 100644
index 0000000..73609d3
--- /dev/null
+++ b/datastructure/matlab/modMat1.JPG
diff --git a/datastructure/matlab/modMat2.JPG b/datastructure/matlab/modMat2.JPG
new file mode 100644
index 0000000..1dca79d
--- /dev/null
+++ b/datastructure/matlab/modMat2.JPG
diff --git a/datastructure/matlab/repMat.JPG b/datastructure/matlab/repMat.JPG
new file mode 100644
index 0000000..917e7a6
--- /dev/null
+++ b/datastructure/matlab/repMat.JPG
diff --git a/datastructure/scilab/call.sci b/datastructure/scilab/call.sci
new file mode 100644
index 0000000..e902744
--- /dev/null
+++ b/datastructure/scilab/call.sci
@@ -0,0 +1,6 @@
+  u = idinput(1024,'PRBS',[0 1/20],[-1 1])

+  a = [1 0.5];b = [0 2 3];

+  model = idpoly(a,b,'Ts',0.1)

+  y = sim(u,model) + rand(length(u),1)

+  plantData = iddata(y,u,0.1)

+  sys = armaX(plantData,[2,2,1])

diff --git a/datastructure/scilab/datSci.JPG b/datastructure/scilab/datSci.JPG
new file mode 100644
index 0000000..6bcffc4
--- /dev/null
+++ b/datastructure/scilab/datSci.JPG
diff --git a/datastructure/scilab/datSci1.JPG b/datastructure/scilab/datSci1.JPG
new file mode 100644
index 0000000..9da52fe
--- /dev/null
+++ b/datastructure/scilab/datSci1.JPG
diff --git a/datastructure/scilab/modSci1.JPG b/datastructure/scilab/modSci1.JPG
new file mode 100644
index 0000000..025d836
--- /dev/null
+++ b/datastructure/scilab/modSci1.JPG
diff --git a/datastructure/scilab/modSci2.JPG b/datastructure/scilab/modSci2.JPG
new file mode 100644
index 0000000..ea9582f
--- /dev/null
+++ b/datastructure/scilab/modSci2.JPG
diff --git a/datastructure/scilab/scilab_exist.aux b/datastructure/scilab/scilab_exist.aux
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diff --git a/datastructure/scilab/scilab_exist.pdf b/datastructure/scilab/scilab_exist.pdf
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+++ b/datastructure/scilab/scilab_exist.pdf
diff --git a/datastructure/scilab/scilab_exist.tex b/datastructure/scilab/scilab_exist.tex
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+\documentclass[a4paper,12pt]{report}

+\usepackage{graphicx}

+\usepackage{booktabs}

+\usepackage{amsmath}

+\usepackage{listings}

+	\title{Scilab Identification Toolbox - Existing features}

+\begin{document}

+	

+

+	

+\maketitle

+	\tableofcontents		

+	

+	

+\chapter{Calling Sequence}	

+	\lstinputlisting{call.sci}	

+%		\begin{figure}[tbp]

+%		\centering

+%		\includegraphics[width=0.8\textwidth]{codeSci.JPG}

+%		\caption{Calling Sequence}

+%		\label{}

+%	\end{figure}

+	

+\chapter{Model structure (idpoly)}

+	

+	\section{Model structure output}

+	\begin{enumerate}

+		\item Model representation (A,B,C polynomials in increasing power of $z^{-1}$)

+		

+	\item  Sampling time 

+	\item Performance of model - Values of Mean Squared Error(MSE), Final Prediction Error(FPE), Fit Percentage, Raw Akaike's Information Criterion(AIC), Small sample-size corrected (AICc), Normalized AIC(nAIC), Bayesian information criteria(BIC)

+	\end{enumerate}

+\section{Model structure attributes}

+\begin{enumerate}

+	\item Polynomial coefficients vector

+	\item Variable('$z^{-1}$') used in polynomials

+	\item Time unit

+	\item Smpling time

+	\item Report giving values of MSE, FPE, Fit Percentage, AIC, AICc, AICn, BIC

+\end{enumerate}

+	

+

+	

+	\begin{figure}[tbp]

+		\centering

+		\includegraphics[width=0.8\textwidth]{modSci1.JPG}

+		\caption{Model(idpoly) Output}

+		\label{}

+	\end{figure}

+	

+	\begin{figure}[tbp]

+		\centering

+		\includegraphics[width=0.8\textwidth]{modSci2.JPG}

+		\caption{Model attributes}

+		\label{}

+	\end{figure}

+	

+	\chapter{Data structure (iddata)}

+	

+	\section{Data structure output}

+	\begin{enumerate}

+		

+		\item Domain of data : Time (No provision of storing frequency domain data in iddata. Another function 'frd' can store frequency and response data)

+		\item Number of samples

+		\item Name of output data vector 

+		

+		\item Name of input data vector

+		

+		\item Sampling time

+		

+	\end{enumerate}

+	Note : No provision of changing name of output or input data vector and time unit.

+	\section{Data struture attributes}

+		

+	\begin{enumerate}

+		\item Output data vector 

+		

+		\item Input data vector

+		

+		\item Sampling time

+		

+		\item Time unit

+	\end{enumerate}

+

+	

+			\begin{figure}[tbp]

+			\centering

+			\includegraphics[width=0.8\textwidth]{datSci1.JPG}

+			\caption{Data(iddata) Output}

+			\label{}

+		\end{figure}

+	

+			\begin{figure}[tbp]

+			\centering

+			\includegraphics[width=0.8\textwidth]{datSci.JPG}

+			\caption{Data attributes}

+			\label{}

+		\end{figure}

+		

+	\chapter{Optimizer}

+	\begin{enumerate}

+		

+		\item diffcode toolbox - Automatic differentiation (consists of functions to evaluate jacobian and hessian but no associated optimizer)

+		\item Optimbase toolbox - building block for optimization methods(number of variables, minimum and maximum bounds, number of non linear inequality constraints, cost function, logging system, various termination criteria)

+		

+		\item Nonlinear Least Squares

+		\begin{enumerate}

+	     	\item lsqrsolve - Levenberg-marquardt algorithm (used in arx, armax, oe) 

+		

+		\item leastsq - Non-linear least squares problem 

+		

+		Algorithms available : quasi-Newton (default), conjugate gradient or non-differentiable

+	

+		

+		

+		

+		\item datafit - Parameter identification based on measured data

+		

+	Algorithms available : quasi-Newton (default), conjugate gradient or non-differentiable

+		

+		

+	\end{enumerate}

+	\item optim - Non-linear optimization

+		

+		Algorithms available : limited memory BFGS algorithm, quasi-Newton method, non-differentiable problems

+		

+		

+		\item karmarkar - Constrained linear optimization problem

+		

+		

+		\item neldermead - Direct search optimization algorithms based on the simplex method

+		

+	\item qpsolve - Quadratic optimization (active set)

+		

+	\item Ipopt - Interior point method

+		

+		\item conjgrad - Conjugate gradient solvers

+	\end{enumerate}

+	

+	

+	

+	

+	

+	

+\end{document}
\ No newline at end of file
diff --git a/datastructure/scilab/scilab_exist.toc b/datastructure/scilab/scilab_exist.toc
new file mode 100644
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+++ b/datastructure/scilab/scilab_exist.toc
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+\contentsline {chapter}{\numberline {1}Calling Sequence}{2}
+\contentsline {chapter}{\numberline {2}Model structure (idpoly)}{3}
+\contentsline {section}{\numberline {2.1}Model structure output}{3}
+\contentsline {section}{\numberline {2.2}Model structure attributes}{3}
+\contentsline {chapter}{\numberline {3}Data structure (iddata)}{5}
+\contentsline {section}{\numberline {3.1}Data structure output}{5}
+\contentsline {section}{\numberline {3.2}Data struture attributes}{5}
+\contentsline {chapter}{\numberline {4}Optimizer}{7}