1. VIDYA PRATISHTHAN’S COLLEGE OF ENGINEERING, BARAMATI.
Adaptive Noise Cancellation using the Neuro-fuzzy Inference System Algorithm.

2. INTRODUCTION… Fuzzy means not conform values.
Fuzzy System
Fuzzy means not conform values.
Fuzzy systems are very useful in two general contexts:
- In situations involving highly complex systems whose behaviors are not well understood.
- In situations where an approximate, but fast, solution is warranted.

3. Fuzzy Inference Systems-
Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic.
The mapping then provides a basis from which decisions can be made, or patterns discerned.
The process of fuzzy inference involves Membership Function, Logical Operations, and If-Then Rules. You can implement two types of fuzzy inference systems

4. The basic principle of noise cancellation using neuro fuzzy is to filter out an interference
component.
By identifying the nonlinear model between
a measurable noise source and the
corresponding immeasurable interference.
The matlab command called ‘ANFIS’ is used to demonstrate how noise cancellation can be applied as interference canceling in a signal.

5. To implement this process of noise cancellation, the software that will be
used is MATLAB.
with the help of the Fuzzy Logic Toolbox, using a very advanced algorithm called the adaptive neuro-fuzzy inference system that is ‘ANFIS’.

6. METHODOLOGY
The main function of adaptive noise cancellation is to filter out an interference parameter.
We studied two types of Fuzzy inference system [FIS] are :
1) Mamdani-type FIS
2)Sugeno-type FIS
In this project we used sugeno FIS.

7. The most fundamental difference between Mamdani-type FIS and Sugeno-type FIS is the way the crisp output is generated from the fuzzy inputs.
Mamdani-type FIS uses the technique of defuzzification of a fuzzy output.
Sugeno-type FIS uses weighted average to compute the crisp output.

8. Block diagram : Rule base input Fuzzification interface
Defuzzification interface
crisp
output
Decision making
fuzzy
fuzzy

9. Construction of Fuzzy Models….
To design a T-S fuzzy controller, we need a
T- S fuzzy model for a nonlinear system.
In general there are two approaches for constructing fuzzy models:-
1. Identication (fuzzy modeling) using input- output data.
2. Derivation from given nonlinear system equations.

10. A rule base containing a number of fuzzy if- then rules.
A database which defines the membership functions of the fuzzy sets used in fuzzy rules.
A decision-making unit which performs the inference operations on the rules.

11. A fuzzification interface which transforms the crisp inputs into linguistic values.
A defuzzification interface which transform the fuzzy results of the inference into a crisp output.
The rule base and the database are jointly referred to as the knowledge base.

12. RULES…
if-then rule statements are used to formulate the conditional statements that comprise fuzzy logic.
-if x is A then y is B
where A and B are crisp value.
Crisp is nothing but hard boundries where as fuzzy is soft boundries of particular function.

13. The if-part of the rule "x is A" is called the antecedent or premise.
while the then-part of the rule "y is B" is called the consequent or conclusion.
An example of such a rule might be “If service is good then tip is average.”

14. Membership Functions:
A Membership Function(MF) is a curve that defines how each point in the input space is mapped to a membership value between 0 and 1.
The function itself can be an arbitrary curve whose shape we can define as a function that suits us from the point of view of simplicity, convenience, speed, and efficiency.

15. The basic Membership functions are:
Piece-wise linear functions.
The Gaussian distribution function.
The sigmoid curve.
Quadratic and cubic polynomial curves.
The degree an object belongs to a fuzzy set is denoted by a membership value between 0 and 1.

20. We have repeated this process of noise cancellation by using : Different Information Signal:-
1)Sine Wave
2)Cos Wave
3)Triangular Wave
4)Wave File
Different Noise :-
1)Random Noise
2)Gaussian Noise

21. Conclusion…
It have been concludes that fuzzy logic based on adaptive noise filtering technique gives the better results as compared to the existing technique.
Adaptive noise cancellation using ANFIS
has been implemented on audio speech
signal.

22. This method is more efficient to eliminate noise and has faster convergence time , low computation load and fewer memory requierments.
The anfis algorithm helps better signal-to- noise ratio in electronic apparatus such as the heart monitor system.
The electronic system which has less noise is better, hence the anfis helps in systems to cancel unwanted signal such as noise using fuzzy logic.

23. LITERATURE SURVEAY Sr. No. Name of paper Published by Methodology 1.
Fuzzy Based New Algorithm For Noise Removal And Edge Detection
International journal of advanced research in computer science and
technology(IJARCST2014)
This paper presents a two stage fuzzy based noise reduction –cum-edge detection filter i.e.INAFSM (image & noise adaptive fuzzy switching median) filter for efficient removal of impulse noise from gray scale images. 2.
Fuzzy Logic based Adaptive Noise Filter for Real Time Image Processing Applications
International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 7, September
ISSN:
This paper consist of of three steps, in the 1st step, center  pixel of the window is tested whether impulse noise is present or not. In the 2nd step, we replace the noisy pixels by using median filters. In 3rd stage we create a histogram if the image and again remove the noise by using soft thresholding. 3.
Intelligent Adaptive Filtering For Noise
Cancellation
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
Vol. 2, Issue 5,May 2013 
This paper describes an intelligent adaptive filtering for noise cancellation. Here ANFIS method is being used for removal of noise from audio speech signals. An audio signal contaminated with noise is taken and inspected with eight types of membership functions.

24. References:
[1] Fuzzy Based New Algorithm For Noise Removal And Edge Detection
International journal of advanced research in computer science and technology(IJARCST2014)
[2] Fuzzy Logic based Adaptive Noise Filter for Real Time Image Processing Applications International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 7, September – 2012 ISSN:
[3] Intelligent Adaptive Filtering For Noise
Cancellation
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 5,May 2013 

25. thank YOU.. !