Neural Networks: Least Mean Square (LSM) Algorithm
1. CHAPTER 03
THE LEAST-MEAN SQUARE
ALGORITHM
CSC445: Neural Networks
Prof. Dr. Mostafa Gadal-Haqq M. Mostafa
Computer Science Department
Faculty of Computer & Information Sciences
AIN SHAMS UNIVERSITY
(most of figures in this presentation are copyrighted to Pearson Education, Inc.)
2. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Introduction
Filtering Structure of the LMS Algorithm
Unconstrained Optimization: A Review
Method of Steepest Descent
Newton’s Method
Gauss-Newton Method
The Least-Mean Square Algorithm
Computer Experiment
2
Model Building Through Regression
3. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Introduction
In Least-Mean Square (LMS) , developed by Widrow
and Hoff (1960), was the first linear adaptive-
filtering algorithm (inspired by the perceptron) for
solving problems such as prediction:
Some features of the LMS algorithm:
Linear computational complexity with respect to adjustable
parameters.
Simple to code
Robust with respect to external disturbance
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4. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Filtering Structure of the LMS Algorithm
Unknown dynamic system described by:
Figure 3.1 (a) Unknown dynamic system. (b) Signal-flow graph of adaptive model
for the system; the graph embodies a feedback loop set in color.
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T
M ixixix )](),...,(),([ 21x
,...,...,1),(),(: niidiΤ x
where
M is the input dimensionality
The stimulus vector x(i) can arise in
either way of the following:
Snapshot data, The M input elements of x
originate at different point in space
Temporal data, The M input elements of x
represent the set of present and (M-1) past
values of some excitation.
5. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Filtering Structure of the LMS Algorithm
The problem addressed is “how to design a multi-
input-single-output model of the unknown dynamic
system by building it around a single linear neuron”
Adaptive filtering (system identification):
Start from an arbitrary setting of the adjustable weights.
Adjustment of weights are made on continuous basis.
Computation of adjustments to the weight are completed inside
one interval that is one sampling period long.
Adaptive filter consists of two continuous processes:
Filtering process: computation of the output signal y(i) and the
error signal e(i).
Adaptive process: The automatic adjustment of the weights
according to the error signal.
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6. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Filtering Structure of the LMS Algorithm
These two processes together constitute a feedback
loop acting around the neuron.
Since the neuron is linear:
where
The error signal
is determined by the cost function used to derive the
adaptive-filtering algorithm, which is closely
related to optimization process.
6
)()()()()()()(
1
iiiyixiwiviy T
M
k
kk xw
T
M iwiwiwi )](),...,(),([)( 21w
)()()( iyidie
7. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Unconstrained Optimization: A Review
Consider the cost function E (w) that is continuously
differentiable function of unknown weights w. we need to find
the optimal solution w* that satisfies:
That is, we need to solve the unconstrained-optimization
problem stated as:
“Minimize the cost function E (w) with respect to the weight
vector w”
The necessary condition for optimality:
Usually the solution is based on the idea of local iterative
descent:
Starting with a guess w(0), generate a sequence of weight
vectors w(1), w(2), …, such that:
7
)(*)( ww EE
0*)( wE
))(())1(( nn ww EE
8. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Unconstrained Optimization: A Review
Method of Steepest descent
The successive adjustments applied to the weight vector w are
in the direction of steepest descent; that is in a direction
opposite to the gradient of E (w).
If g = E (w) , then the steepest descent algorithm is
Where is a positive constant called the step size, or learning-
rate, parameter. In each step, the algorithm applies the
correction:
HW: Prove the convergence of this algorithm and show how it
is influenced by the learning rate . (Sec. 3.3 of Haykin)
8
)()()1( nnn gww
)(
)()1()(
n
nnn
g
www
9. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Unconstrained Optimization: A Review
When is small, the transient response of the algorithm is
overdamped, i.e., w(n) follows smooth path.
When is large, the transient response of the algorithm is
underdamped, i.e., w(n) follows zigzaging (oscillatory) path.
When exceeds critical value, the algorithm becomes unstable.
Figure 3.2 Trajectory of the method of steepest descent in a two-dimensional space for two
different values of learning-rate parameter: (a) small η (b) large η .The coordinates w1 and w2
are elements of the weight vector w; they both lie in the W -plane.
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10. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Unconstrained Optimization: A Review
Newton’s Method
The idea is to minimize the quadratic approximation of the
cost function E (w) around the current point w(n):
Where H(n) is the Hessian matrix of E (w)
The weights are updated by
minimizing E (w) as:
10
)()()(
2
1
)()(g
))(())1(())((
T
nnnnn
nnn
T
wHww
www
EEE
2
2
2
2
1
2
2
2
2
2
2
12
2
1
2
21
2
2
1
2
2
))(()(
MMM
M
M
wwwww
wwwww
wwwww
nn
EEE
EEE
EEE
E
wH
)()()(
)()()1(
1
nnn
nnn
gHw
www
11. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Unconstrained Optimization: A Review
Newton’s Method
Generally, Newton’s method converges quickly and does not
exhibit the zigzagging behavior of the method of steepest-
descent.
However, Newton’s method has two main disadvantages:
the Hessian matrix H(n) has to be a positive definite matrix for
all n, which is not guaranteed by the algorithm.
This is solved by the modified Gauss-Newton method.
It has high computational complexity.
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12. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
The Least-Mean Square Algorithm
The aim of the LMS algorithm is to minimize the
instantaneous value of the cost function E (w) :
Differentiation of E (w), with respect to w, yields :
As with the least-square filters, the LMS operates on linear
neuron, we can write:
and
Finally
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)(
2
1
)ˆ( 2
newE
ww
w
)(
)(
ˆ
)ˆ( ne
ne
E
)(
ˆ
e(n)
)()(ˆ)()( nnnidne T
x
w
xw
)()(
ˆ
)ˆ(
)( nenn x
w
w
g
E
)()()(ˆ)()(ˆ)1(ˆ nennngnn xwww
13. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
The Least-Mean Square Algorithm
The inverse of the learning-rate acts as a memory of the LMS
algorithm. The smaller the learning-rate , the longer the
memory span over the past data,
which leads to more accurate results but with slow convergence rate.
In the steepest-descent algorithm the weight vector w(n) follows
a well-defined trajectory in the weight space for a prescribed .
In contrast, in the LMS algorithm, the weight vector w(n) traces a
random trajectory. For this reason, the LMS algorithm is
sometimes referred to as “stochastic gradient algorithm.”
Unlike the steepest-descent, the LMS algorithm does not require
knowledge of the statistics of the environment. It produces an
instantaneous estimate of the weight vector.
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14. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
The Least-Mean Square Algorithm
Summary of the LMS Algorithm
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15. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Virtues and Limitation of the LMS Alg.
Computational Simplicity and Efficiency:
The Algorithm is very simple to code, only two or
three line of code.
the computational complexity of the algorithm is
linear in the adjustable parameters.
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17. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Cost Function
Usually linear models produce concave cost functions
Figure copyright of Andrew Ng.
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18. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
1
0
J(0,1)
Cost Function
Different starting point could lead to different solutionFigure copyright of Andrew Ng.
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19. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
The corresponding solution (Contour of the cost function
Finding a Solution
Figure copyright of Andrew Ng.
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28. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Virtues and Limitation of the LMS Alg.
Robustness
Since the LMS is model independent, therefore it is robust with
respect to disturbance.
That is, the LMS is optimal in accordance with H norm (the
response of the transfer function T of the estimator).
The philosophy of the optimality is to accommodate with the worst-case
scenario:
• “If you don’t know what you are up against, plan for the worst scenario and
optimize.”
Figure 3.8 Formulation of the optimal H∞ estimation problem. The generic estimation error at the transfer
operator’s output could be the weight-error vector, the explanational error, etc.
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29. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Virtues and Limitation of the LMS Alg.
Factors Limiting the LMS Performance
The primary limitations of the LMS algorithm are:
Its slow rate of convergence (which become serious when the
dimensionality of the input space becomes high), and
Its sensitivity to variation in the eigen structure of the input. (it
typically requires a number of iterations equal to about 10 times the
dimensionality of the input data space for it to converge to a stable
solution.
• The sensitivity to changes in environment become particularly
acute when the condition number of the LMS algorithm is high.
• The condition number,
(R) = max /min,
• Where max and min are the maximum and minimum
eigenvalues of the correlation matrix, R xx.
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30. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Learning-rate Annealing Schedules
The slow-rate convergence may be
attributed to maintaining the learning-
rate parameter, , constant.
In stochastic approximation, the
learning-rate parameter is time-
varying parameter
The most common form used is:
An alternative form, is the search-then-
converge schedule.
Figure 3.9 Learning-rate annealing
schedules: The horizontal axis,
printed in color, pertains to the
standard LMS algorithm.
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n
c
n )(
)/(1
)(
n
n o
31. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Computer Experiment
d=1
Figure 3.6 LMS classification with distance 1, based on the double-moon
configuration of Fig. 1.8.
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32. ASU-CSC445: Neural Networks Prof. Dr. Mostafa Gadal-Haqq
Computer Experiment
d=-4
Figure 3.7 LMS classification with distance –4, based on the double-moon
configuration of Fig. 1.8.
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