\newpage \noindent {\bf \huge taksug}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
class for implementing the taksug fuzzy functions
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=taksug(varargin)}\\ 
{\tt m=taksug(taksug\_obj)}\\ 
{\tt m=taksug(structure)}\\ 
{\tt m=taksug(name, n\_inputs, n\_outputs)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=taksug(varargin)} 
returns an empty taksug object.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt m=taksug(taksug\_obj)} 
returns a copy of the {\tt taksug\_obj} object.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt m=taksug(structure)} 
initializes a new taksug object
with the appropriate fields set according to the values
defined in the fields of {\tt structure}.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt m=taksug(name, n\_inputs, n\_outputs)} 
initializes a new taksug
object called {\tt name}, with  number of inputs {\tt n\_inputs} and
number of outputs {\tt n\_outputs}.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
The taksug class is a child of the mapping class and allows the
definition of a static system using a fuzzy rule based
Takagi-Sugeno (TS) representation. 
The class inherits all the fields of the mapping class and defined a 
series of new attibutes required for implementing the TS
representation of a model. At the top level the following fields are
defined:
\\  
\begin{tabular}{lp{12cm}}
{\tt n\_rules}: & defines the number of fuzzy rules of the model \\ 
{\tt model\_code}: & defines the structure of the fuzzy rules \\ 
{\tt centers}: & position of the center of each rule \\ 
{\tt ivariances}: & inverse of the projected covariance matrix
of the cluster associated to each rule. In other words, 
it is the quadratic matrix defining the local metric (or 
the shape) of the rule \\ 
{\tt linears}: & hyperplane associated to each rule  (consequence of the rule) \\ 
{\tt m}: & index of the fuzzyness of the model \\ 
{\tt mfs}: & description of the fuzzy sets while they are defined along each dimension. \\ 
{\tt rls}: & description of the rules base. \\ 
\end{tabular} \\ 

\end{description}

\begin{description}
 \item[Methods]\ \\ 
\begin{itemize}
\item {\tt add\_rules}   adds specified number of rules
\item {\tt add\_sets}   Add fuzzy sets along one or more dimensions of the input space

\item {\tt check}   checks the consistency of all the field of a taksug object
\item {\tt denormalize}   denormalizes the model
\item {\tt display}   Display an object of class taksug

\item {\tt drawcontrols}   Draw controls which allow to control the plot behaviour
\item {\tt eval}   computes the value of the model
\item {\tt fit\_linears}   identifies the consequent part of a fuzzy model from data
\item {\tt get}   gets the value of the attribute of an object
\item {\tt identify}   identifies a fuzzy model from data
\item {\tt interp\_model}   Computes a local fuzzy combination of the consequents of the rules

\item {\tt jacob\_inputs}   computes the jacobian of the model
\item {\tt jacob\_params}   computes the jacobian of the model
\item {\tt lev\_marq}   Performs a Levenbergh Marquardt optimisation of the mapping

\item {\tt membership}   returns the membership of a rule
\item {\tt normalise}   normalises the model M
\item {\tt plot}   performs a plot of the a taksug model
\item {\tt plot}   performs a plot of the a taksug model
\item {\tt rem\_rules}   remove the specified rules from the taksug object

\item {\tt set}   set object properties
\item {\tt sets\_grid}   Remove every fuzzy sets and replace them by evenly spaced ones

\end{itemize}
\end{description}
\newpage \noindent {\bf \huge add\_rules}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
adds specified number of rules
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt add\_rules(m,n)  }\\ 
{\tt add\_rules(m,n,model\_code)  }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt add\_rules(m,n)  } 
add {\tt n} rules to the model {\tt m}.
When rules are added the {\tt optimparams} property of the model is set to
contain the parameters of the newly added rules. This allow to optimise by default
every parameters of a TS system.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt add\_rules(m,n,model\_code)  } 
add {\tt n} rules to the model {\tt m}, {\tt model\_code} is 
a cell array {x y z}. x is the type of TS model ('crossproduct' or  'projected'). For 'crossproduct' models,
y is the type of the membership functions  ('gaussian', 'inversedist': \begin{equation}
 \frac{1}{distance of mahalanobis^{\frac{1}{m-1}}} 
\end{equation}
 and z is the type of the consequents 
('linear'). For 'projected' systems, y specify the way rules are initially positioned  ('standard': no positionment, 'rulegrid': rules evenly disposed on a grid defined by the fuzzy sets)  and z is the type of the consequents 
('linear'). Only rules of the same type can co-exist in a TS model.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
This function is used to 
specify the number and shape of the rules associated to the
TS model. 

\end{description}
\newpage \noindent {\bf \huge add\_sets}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Add fuzzy sets along one or more dimensions of the input space

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m = add\_sets(m,i,n,t)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m = add\_sets(m,i,n,t)} 
adds {\tt n} sets to the dimension {\tt i}
of the Takagi Sugeno Function {\tt m}. The sets are of type t
Accepted values for {\tt t} are 'constant, 'trapezoidal','gaussian'
and 's-shaped'. This method is used to define the fuzzy sets for the 'projected'
type of fuzzy model.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
add\_rules

\end{description}
\newpage \noindent {\bf \huge check}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
checks the consistency of all the field of a taksug object
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt check(m)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt check(m)} 
check the consistency of a taksug object.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
This function has been provided in order to make the taksug class
more robust since it checks a taksug object for consistency of
the data defined in its fields. If a inconsistency is found, an 
error message is displayed.

\end{description}
\newpage \noindent {\bf \huge denormalize}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
denormalizes the model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=denomalise(m)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=denomalise(m)} 
denormalzes the model {\tt m}.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
This function 
can be applied to denormalize a model that has been previously 
normalised with the function {\tt normalize}
restoring the original shape of the local representation of the
model.

\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
normalize

\end{description}
\newpage \noindent {\bf \huge display}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Display an object of class taksug

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt display(m)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt display(m)} 
Display the object m of class taksug
%
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 


\end{description}
\newpage \noindent {\bf \huge drawcontrols}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Draw controls which allow to control the plot behaviour
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt drawcontrols(m,f,a,bounds)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt drawcontrols(m,f,a,bounds)} 
binds a series of controls drawed inside {\tt bounds},
to the figure {\tt f} and the axes {\tt a}.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
plot

\end{description}
\newpage \noindent {\bf \huge eval}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
computes the value of the model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt out=eval(m,x,j)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt out=eval(m,x,j)} 
returns the value {\tt out} for the output(s) {\tt j} of
model {\tt m} given the input {\tt x}.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
This function
returns the output of system as computated by the model, given 
a regressor. 

\end{description}
\newpage \noindent {\bf \huge fit\_linears}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
identifies the consequent part of a fuzzy model from data
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=fit\_linears(m,in,out,type) }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=fit\_linears(m,in,out,type) } 
identify model {\tt m} on the basis of the data
inside {\tt in} and {\tt out}.{\tt type} is used to
specify the type of the fit. 0 is the
defaults and leads to a classical LMS fit. 1 leads to a local fit.
\end{description}
\newpage \noindent {\bf \huge get}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
gets the value of the attribute of an object
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt d = get(m,label)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt d = get(m,label)} 
gets the value of the attribute {\tt label}
associated to the object {\tt m} and return it inside {\tt d}.
The following codes are recognised:
\\  
\begin{tabular}{lp{12cm}}
{\tt name}: & returns the name of the object \\ 
{\tt n\_in}: & returns the number of inputs of the mapping \\ 
{\tt n\_out}: & returns the number of outputs of the mapping \\ 
{\tt userData}: & returns the 'userData' field \\ 
{\tt opt}: & returns the 'opt' field \\ 
{\tt date}: & returns the date of creation of the object \\ 
{\tt limits}: & returns the limits of the mapping \\ 
{\tt optimparams}: & returns the indices of the parameters to be optimised \\ 
{\tt numparams}: & returns the number of parameters to be optimised \\ 
{\tt n\_rules}: & returns the number of rules associated to the
taksug model \\ 
{\tt model\_code}: & returns the code of the model associated to the
taksug model \\ 
{\tt m}: & returns the fuzziness associated to the
taksug model \\ 
{\tt centers}: & returns the positions of the centers of the
rules  associated to the taksug model \\ 
{\tt ivariances}: & returns the invariances of the
rules  associated to the taksug model \\ 
{\tt linears}: & returns the linears of associated to the taksug
model \\ 
{\tt rls}: & returns the fuzzy sets associated to the taksug
model \\ 
{\tt mfs}: & returns the definition of the rules of the taksug model \\ 
{\tt params}: & returns every parameters to be optimised in the shape of a vector \\ 
\end{tabular} \\ 

\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
set

\end{description}
\newpage \noindent {\bf \huge identify}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
identifies a fuzzy model from data
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=identify(m,in,out,options) }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=identify(m,in,out,options) } 
identify model {\tt m} on the basis of the data
inside {\tt in} and {\tt out} using method specified in {\tt options}.
The accepted fields for options are:
\\  
\begin{tabular}{lp{12cm}}
{\tt method}: & identification method(see below). \\ 
{\tt n\_rules}: & number of rules. \\ 
{\tt min\_n\_rules}: & min. nb. of rules (incremental methods only) \\ 
{\tt max\_n\_rules}: & max. nb. of rules (incremental methods only) \\ 
{\tt fuzzyness}: & fuzzyness of the model (usually greater than 1) \\ 
{\tt tolerance}: & minimal improvement for recursive techniques before the algorithm stops. \\ 
{\tt seed}: & seed for random generator \\ 
{\tt lin\_fit}: & type of linear fitting: 0: normal, 1: local \\ 
{\tt rule\_type}: & rule type code (see {\tt add\_rules} for more info) \\ 
\end{tabular} \\ 

Methods of identification are:
\\  
\begin{tabular}{lp{12cm}}
{\tt cluslms}: & GK clustering followed by a LMS fit \\ 
{\tt fmclust}: & Robert Babuska's identification method \\ 
{\tt incrsie}: & Incremental identification method (Siemens) \\ 
{\tt tsgklmxv}: & incremental method developped in IRIDIA \\ 
{\tt cluslev}: & GK clust. init. and Lev.-Marq. optimisation \\ 
{\tt randlm}: & random init. and Lev.-Marq. optimisation \\ 
\end{tabular} \\ 

\end{description}
\newpage \noindent {\bf \huge interp\_model}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Computes a local fuzzy combination of the consequents of the rules

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt l=interp\_model(m,x,j)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt l=interp\_model(m,x,j)} 
computes for {\tt taksug} model {\tt m}, a fuzzy combination
of the consequents of the rules at point {\tt x}. {\tt l} is equal to
a linear combination of the parameters of the consequents for each rule. The coefficient 
of the linar combination are equal to the membership of {\tt x} to the different rules.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
also jacob\_params 

\end{description}
\newpage \noindent {\bf \huge jacob\_inputs}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
computes the jacobian of the model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt jacob\_inputs(m,x)}\\ 
{\tt jacob\_inputs(m,x,j)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt jacob\_inputs(m,x)} 
computes for each output the jacobian of
the model with respect to the x (the input of the
linear) at point {\tt x}.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt jacob\_inputs(m,x,j)} 
computes for output J the jacobian of
the model with respect to the x (the input of the
linear) at point {\tt x}.
\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
The procedure for computing the jacobian depends on the underlying representation 
of the model. The result {\tt out} is an array of size number of outputs * number of inputs
containing the derivatives of the outputs with respect to the inputs.

\end{description}
\newpage \noindent {\bf \huge jacob\_params}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
computes the jacobian of the model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt jacob\_params(m,x)  }\\ 
{\tt jacob\_params(m,x,j)  }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt jacob\_params(m,x)  } 
computes for each output the jacobian of
the model with respect to the parameters at input {\tt x}.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt jacob\_params(m,x,j)  } 
computes for output(s) {\tt j} the jacobian of
the model with respect to the parameters at input {\tt x}.
\end{description}
\newpage \noindent {\bf \huge lev\_marq}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Performs a Levenbergh Marquardt optimisation of the mapping

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt function m = lev\_marq(m, in, out, cost)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt function m = lev\_marq(m, in, out, cost)} 
Optimises the mapping {\tt m} with respect to input/outpu couples
<CODE>in/out</CODE>.
This method is mainly used internaly. Identify should be prefered.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
identify

\end{description}
\newpage \noindent {\bf \huge membership}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
returns the membership of a rule
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt out=membership(m,x,j) }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt out=membership(m,x,j) } 
returns the membership of the rules for the model {\tt m}.
\end{description}
\newpage \noindent {\bf \huge normalise}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
normalises the model M
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=normalise(m)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=normalise(m)} 
normalises the model {\tt m}
\end{description}
\newpage \noindent {\bf \huge plot}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
performs a plot of the a taksug model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt f=plot(m) }\\ 
{\tt f=plot(m,options) }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt f=plot(m) } 
opens a new figure if needed and plots the model
{\tt m}. A complete user interface is set to allow advanced visualisation
options to be selected.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt f=plot(m,options) } 
opens a new figure if needed and plots the model
{\tt m}. {\tt options} are passed to the drawing engine.
\end{description}
\newpage \noindent {\bf \huge plot}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
performs a plot of the a taksug model
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt f=plot(m) }\\ 
{\tt f=plot(m,data) }\\ 
{\tt f=plot(m,data,options) }\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt f=plot(m) } 
opens a new figure if needed and plots the model
{\tt m}. A complete user interface is set to allow advanced visualisation
options to be selected.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt f=plot(m,data) } 
opens a new figure if needed and plots the model
{\tt m}. The data specified in {\tt data} are plotted on the same graph in order to
visually X validate the model.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt f=plot(m,data,options) } 
opens a new figure if needed and plots the model
{\tt m}. The data specified in {\tt data} are plotted on the same graph in order to
visually X validate the model. {\tt options} are passed to the drawing engine.
\end{description}
\newpage \noindent {\bf \huge rem\_rules}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
remove the specified rules from the taksug object

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=rem\_rules(m)}\\ 
{\tt m=rem\_rules(m,n)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=rem\_rules(m)} 
remove all rules from model {\tt m}.
\vspace{4mm}\\ 
\hspace{2mm}
{\tt m=rem\_rules(m,n)} 
remove rules number {\tt n} from model {\tt m}.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 


\end{description}
\newpage \noindent {\bf \huge set}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
set object properties
\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m=set(m,label1,value1,label2,value2,...)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m=set(m,label1,value1,label2,value2,...)} 
sets the value {\tt value} of the attribute {\tt label}
associated to the object {\tt m}. The following codes are recognised:
\\  
\begin{tabular}{lp{12cm}}
{\tt name}: & sets the name of the object \\ 
{\tt n\_in}: & sets the number of inputs of the model. This
sets the saturation level of the inputs to 
-Inf, Inf. \\ 
{\tt n\_out}: & sets the number of outputs of the model \\ 
{\tt userData}: & sets the 'userData' field \\ 
{\tt opt}: & sets the 'opt' field \\ 
{\tt date}: & sets the date of creation of the object \\ 
{\tt limits}: & sets the limits of the mapping \\ 
{\tt optimparams}: & sets the indices of the parameters to be optimised \\ 
{\tt model\_code}: & sets the type of rules associated to the
taksug model \\ 
{\tt m}: & sets the fuzziness associated to the
taksug model \\ 
{\tt centers}: & sets the positions of the centers of the
rules  associated to the taksug model \\ 
{\tt center}: & sets the positions of one center of the
rules  associated to the taksug model. {\tt value}
is of the form {number value}. \\ 
{\tt ivariances}: & sets the invariances of the
rules  associated to the taksug model \\ 
{\tt ivariance}: & sets the positions of one ivariance of the
rules  associated to the taksug model. {\tt value}
is of the form {number value}. \\ 
{\tt linears}: & sets the linears of associated to the taksug
model \\ 
{\tt linear}: & sets the positions of one linear of the
rules  associated to the taksug model. {\tt value}
is of the form {number value}. \\ 
{\tt rls}: & sets the fuzzy sets associated to the taksug
model \\ 
{\tt rl}: & sets one fuzzy set associated to the taksug
model \\ 
{\tt mfs}: & sets the definition of the rules of the taksug model \\ 
{\tt mf}: & sets the definition of one rule of the taksug model \\ 
{\tt params}: & sets every parameters to be optimised providing a vector \\ 
{\tt optimselect}: & basically achieves the same result as {\tt optimparams}
but lets the user perform a grouped selection of the parameters
(all the consequents parameters, all the centers,...), {\tt value}
is 4 bits binary number dcba. a is set for selecting the centers, b for the
ivariances, c for the consequents except the offsets, d for the offsets \\ 
\end{tabular} \\ 

\end{description}

\begin{description}
 \item[Remarks]\ \newline \newline 
General purpose function for 
setting the values of the fields of a taksug object. For setting 
the rules, use the {\tt add\_rules} and {\tt rem\_rules} function. This method
extends the 'set' method defined in the mpping class. 

\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
get, add\_rules, rem\_rules

\end{description}
\newpage \noindent {\bf \huge sets\_grid}\\  \rule[3pt]{\textwidth}{1pt}\\
\begin{description}
 \item[Purpose]\ \newline \newline 
Remove every fuzzy sets and replace them by evenly spaced ones

\end{description}

\begin{description}
 \item[Synopsis]\ \newline \newline 
{\tt m = sets\_grid(m, d1, d2, d3, d4..., t)}\\ 
\end{description}

\begin{description}
 \item[Description]\ \newline \newline 
{\tt m = sets\_grid(m, d1, d2, d3, d4..., t)} 
places {\tt d1} sets of type {\tt t} along the dimension {\tt 1},
{\tt d2} sets along the dimension {\tt 2}, etc... The position of the
sets is computed on the basis of the limits attribute. The previously defined rules and
sets are removed.
\end{description}

\begin{description}
 \item[See also]\ \newline \newline 
add\_setss

\end{description}
