This is the internal function that implements the dynamic evolving neuralfuzzy inference system (DENFIS).
It is used to handle regression tasks. Users do not need to call it directly,
but just use frbs.learn
and predict
.
1 2  DENFIS(data.train, range.data.ori, Dthr = 0.1, max.iter = 100,
step.size = 0.01, d = 2)

data.train 
a matrix (m \times n) of data for the training process, where m is the number of instances and n is the number of variables (input and output variables). 
range.data.ori 
a matrix (2 \times n) containing the range of the data, where n is the number of variables, and first and second rows are the minimum and maximum values, respectively. 
Dthr 
the threshold value for the evolving clustering method (ECM), between 0 and 1. 
max.iter 
the maximal number of iterations. 
step.size 
the step size of the least squares method, between 0 and 1. 
d 
a parameter for the width of the triangular membership function. 
This method was proposed by Nikola K. Kasabov and Q. Song. There are several steps in this method that are to determine the cluster centers using the evolving clustering method (ECM), to partition the input space and to find optimal parameters on the consequent part (Takagi Sugeno Kang model) for the IFTHEN rule using a least squares estimator.
ECM is a distancebased clustering method which is determined by a threshold value, Dthr
. This parameter
influences how many clusters are created. In the beginning of the clustering process, the first instance from the
training data is chosen to be a cluster center, and the determining radius is set to zero. Afterwards, using the
next instance, cluster centers and radius are changed based on certain mechanisms of ECM (please see ECM
).
All of the cluster centers are then obtained after evaluating all the training data.
The next step is to update the parameters on the consequent part with the assumption that the antecedent part which we got from ECM is fixed.
Actually, ECM can perform well as an online clustering method, but in this package it is used in an offline mode.
N.K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neuralfuzzy inference system and its Application for timeseries prediction", IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144  154 (2002).
DENFIS.eng
, frbs.learn
, and predict
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