A neural network structure of ANFIS
is shown in Figure 1.
FSVRN model and Jang's ANFIS
model  with 400 training data are listed in Table 1.
techniques (Chiu 1996) outlined above have allowed to perform some experiments whose results were reported in Tables 1-3.
2011) to develop ANFIS
, ANN and FIS-based prediction models for the ultimate bearing capacity.
is more effective than conventional classifiers such as multiple linear regressions in classifying patterns in which the input is noisy and the system is not well defined.
The main aim of this paper is to investigate the capability of an ANFIS
in modeling gold price changes and to evaluate its performance in comparison with ANN and other traditional time series modeling techniques such as ARIMA.
A synthetic data would be generated and presented to ANFIS
to classify between the normal and attack traffic.
For controlling the temperature and humidity of an HVAC system using PID controllers, an intelligent approach for modeling and the control of the system was achieved by Soyguder and Alli (2009b), using ANFIS
, leading, in particular, to a more accurate prediction of damper gap rate and faster and simplified solutions.
was designed as a single output system, thus each ANFIS
system can only successfully diagnose one type of machine fault .
An option is to update the decision boundaries to match the raw-material quality in smaller regions, for example by applying ANFIS
(Adaptive Neuro Fuzzy Inference Systems) (Jang 1993).
The idea of fuzzy control of the parallel robots was tested and demonstrated in ANFIS
MATLAB environment tool .
In this paper, a multi-objective optimization method, based on combination of ANFIS
and ACO evolutionary algorithms, is proposed to obtain the optimal parameters in turning processes.