this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.

1. Stable Normalized Least Mean Fourth
Algorithm for Adaptive Noise Cancellation
Under the esteemed guidance of
G. Amjad Khan sir M.Tech., (Ph.D)
Asst Prof., Dept of ECE
By 129X1D0918
V Sreenivasa Arun Kumar

2. Aim of the Project
By using NLMF algorithm implement the noise cancellation
and system identification
Statistically analyze the conditions where NLMF is superior
to NLMS

3. Why Adaptive Filter
 To prevent potential loss of information an adaptive
filter could be used
 The quality of output signal is more accurate
 Ex: Pilot in an Airplane

4. What is Meant by Adaptive Filter
 An adaptive filter is a filter that self-adjusts its transfer
function according to an optimization algorithm driven by an
error signal.
The adaptive process involves the use of a cost function,
which is a criterion for optimum performance of the filter, to
feed an algorithm, which determines how to modify filter
transfer function to minimize the cost of the next iteration.

5. What are different families of algorithms
There are two approaches
Stochastic Gradient Least Squares Estimation
1. LMS 1. RLS
2. TV LMS 2. Std. RLS
3. NLMS 3. SQ RLS
4. GAL 4. Fast RLS

6. Basic structure of Adaptive Filter
Error Section
Adaption
Section
Filtering
Section

7. LMS
LMF

8. Why LMF over LMS & NLMS
Trade-off between Transient and steady state updation.
It is given by the equation w(n+1) = w(n) + µe(n) x(n)
If µ is large we may not converge towards optimum
solution that is wopt.
If µ is small a optimum solution wopt can be achieved,
but disadvantage is slow updation.

10. WHY DO WE PREFER LMS..!
 computationally less complexity just order of
Filter length(m)
 Numerically good stability
 Very simple to implement

11. Problem statement while using LMF
Stability depends on Variance and Distribution type
of adaptive filter input.
Stability condition is given by 0 < µ < 2 / λmax
Stability depends on Noise Variance
Stability depends on initial filter weights

12. Stability
1. Numerical Stability
Division by zero
Singular Matrix in
Matrix inversion
2. Convergence Stability
Eigen Values of input
Eigen spread

13. NLMF Algorithm
NLMF algorithm equation is given by
h(n+1) = h(n) +
The Normalizing term is fourth order in regressor and
second order in the estimation error

14. Implementation of NLMF algorithm for Noise
cancellation

15. System Identification
Adaptive Filter
Plant
∑
u
System
input
e
y
d
-
+

16. Flow Chart
Create input
signal
Initialize coeff matrix = 0
Compute i/p matrix
Compute filter o/p
Compute error
Compute coeff matrix