Hi I am Pearl. I would like to get details on anfis matlab source code FOR EEG signal detection. My friend said anfis matlab source code will be available here and now i am living at Sarawak and i last studied in the UNIMAS and now am doing Computational Intelligence assignment. i need help on ANFIS source code for feature extraction and feature classification on epileptic EEG signals using Discrete wavelet transform and ANFIS. I am urgently searching for it. Desperately hope that someone might help out. thanks alot!

introduction
the controller always aims to achieve the process variable to the
given set point value. this is the main task of the properly
designed controller. the controller should also provide some
flexibility in case of change in set point and disturbances. today
there are many methods for designing intelligent controllers, such
as predictive controller, fuzzy control, neural networks and expert
systems. various combinations of these controllers give a number
of design possibilities.


model predictive control
classical mpc
the main idea behind mpc-type controllers is illustrated in
figure 1 for a siso system. at sampling time k, a set of m future
manipulated variable moves (control horizon) are selected, so that
the predicted response over a finite horizon p (prediction horizon)
has certain desirable characteristics. this is achieved by
minimizing an objective function based on the deviation of the
future controlled variables from a desired trajectory over the
prediction horizon p and the control energy over the control
horizon m. the mpc optimization is performed for a sequence of
hypothetical future control moves over the control horizon and
only the first move is implemented [8].



neural networking modeling
neural network needs the system input and output data (figure 2).
neural network is connected parallel to the system and they share
input. second input to neural network is error between system
and neural network output. based on this error, new parameters
of neural network are adjusted. sampling period of input and
output data, network architecture, training algorithm and train
periods number are affect the quality of trained neural network.



distillation column
the distillation column is probably the most popular and
important process studied in the chemical engineering literature.
distillation is used in many chemical processes for separating feed
streams and for the purification of final and intermediate product
streams. most column handle multicomponent feeds, but many
can be approximated by binary or pseudobinary mixtures. the
objective is to split a liquid two component mixture into its
fractions throughout stripping and rectifying processes.



anfis as an estimator
anfis can be used for the estimation of some dependent
variables in chemical process. the designed anfis estimator is
used to infer the compositions from measurable tray temperatures
distillation column. in estimator design process, different anfis
structure are constructed and trained to find the architecture that
gives the best performance as an estimator.


control of the distillation column
the control of distillation column is often considered to be the
benchmark for nonlinear process control because of the highly
nonlinear behavior exhibited by composition dynamics. in this
study, it is aimed to use anfis as a controller in a composition
control system. for this purpose, anfis controller is designed
and used in an adaptive way in the distillation column control
scheme. figure 6 illustrates the adaptive control scheme for the
distillation column control system under study.