# LIFE PREDICTION OF LOGARITHMIC ORMAL DISTRIBUTION BASED ON LSS

【摘要】 a life prediction method based on least squares support vector machine(LSSVM) for small data samples from logarithmic normal distributions. This method can directly predict lifetime by building...

1. Introduction
When dealing with small data samples in lifetime
prediction, there are fundamentally two different analysis
methods: Bayes method and neural network. Bayes
method is based on prior information to make up for the
reliability data limitation [1]. However, it’s difficult to
obtain prior knowledge for modern high reliable integrated
circuit (IC). Accordingly, there are not extensive
researches about the application of Bayes method in IC life
prediction. Way Kuo [2] firstly used neural network to
estimate Weibull distribution parameters for small
reliability data samples. However, it was found that there
were overfitting phenomena when using neural network
with small samples. For small samples, a proper lifetime
prediction method should be selected based on its
generalization, which is defined as a method's ability to fit
current data but also to predict future data [3]. Support
vector machine (SVM) can be used to life prediction
instead of neural network, which overcomes the overfitting
phenomena and has good generalization for small data
samples [4]. Least square support vector machine (LSSVM)
algorithm originally proposed by Suykens [5] is a
modification to SVM, which is easier to use than SVM
because it adopts the least squares linear system as its loss
function. Based on LSSVM, this paper developed a
lifetime prediction method for IC when small data samples
are available that obey logarithmic normal distribution.
In the rest of this paper, the LSSVM –based lifetime
prediction approach description is presented in section 2. In
section 3 Monte Carlo simulations are used to demonstrate
this method. For comparison, Error back propagation (BP)
neural network is also compared with it. The discussion and
conclusions are given in section 42. Prediction based on LSSVM
2.1. Problem description
In the conventional method of lifetime prediction, the
first step is to obtain failure data from a test which places
no constraints on the mean, variance or range. Based on the
test results, a proper constraint is then imposed such that the
data may statistically fit one of the known distributions.
Then the known distribution parameters can be evaluated.
Once the distribution parameters are evaluated accurately,
the lifetime can be predicted according to the known
distribution. However, when dealing with small data
samples, it can’t be guaranteed to obtain accurate
parameters estimate, any potential inaccuracy on
distribution parameters may lead to increase lifetime
uncertainties [6]. Thus, in this paper, the LSSVM-based
prediction method can directly predict lifetime of small data
samples through building a least squares support vector
regression model without the evaluation of distribution

parameters.

2.2. Neural network
Neural networks have been studied by a lot of
researches [7-11]. It is well known that a neural network
is a universal estimator. It has in general, however, two
main drawbacks for its learning process:
(1) The architecture, including the number of hidden
neurons, has to be determined a priori or modified while
training by heuristic.
(2) The training process in neural networks can easily
be stuck by local minima. Various ways of preventing local
minima, like early stopping, weight decay, etc., are
employed. However, those methods greatly affect the
generalization of the estimated function, i.e., the capacity of
handling new input cases.
2.3. Support vector machines
SVM is an interdisciplinary field of optimization,
statistical learning theory, machine learning and data
mining technique [12-14]. Basically, it can be used for
function estimation and pattern classification. Since the
application of SVM in lifetime prediction is related in
function estimation, the discussion is only related to
functions estimation issues.
SVM is a very useful methodology to formulate the
mathematical program for the training error function used
in any application. No matter which application, SVM
formulates the training process as a quadratic programming
problem for the weights with regularization factor included.
Since quadratic programming problem is a convex function,
the solution returned is global instead of many local ones,
unlike neural networks. This result ensures the high
generalization of the trained SVM regression models over
neural networks.
Another important appear of SVM over other
traditional regression methods is its ability to handle very
high nonlinearity. Similar to nonlinear regression, SVM
transforms the low dimensional nonlinear input data space
into high-dimensional linear feature space through a
nonlinear mapping. Then linear function estimation over the
feature space can be performed. The problem now turns to
find out this nonlinear mapping for its primal formulation.
Nevertheless, SVM dual formulation provides an
inner-product kernel trick, which totally eliminates the
effort of finding the nonlinear mapping in the primal
formulation as necessary in traditional nonlinear regression
methods.
2.4. Least squares support vector regression
The major drawback of SVM is its higher
computational burden because of the required constrained
optimization programming. Major breakthrough has been
obtained at this point with a least squares version of SVM,
called LSSVM. In LSSVM, one works with equality
instead of inequality constraints and a sum squared error
cost function as it is frequently used in training of classical
neural networks. This reformulation greatly simplifies the
problem in such a way that the solution is characterized by
a linear system, more precisely a Karush-Kuhn-Tucker
system, which takes a similar form as the linear system that
one solves in every iteration step by interior point methods
for standard SVM. This linear system can be efficiently
solved by iterative methods such as conjugate gradient. So,
this paper uses LSSVM to predict lifetime instead of SVM.
For least squares support vector regression, what we
care mostly is the generalization ability of learning machine.
Good generalization ability means that it can’t only fit
current data but also to predict future data. The
generalization ability of LSSVM is based on the factors
described in structural risk minimization, which has greater
generalization ability and is superior to the empirical risk
minimization as developed for large sample and adopted in
neural networks in the problem of regression estimation.
For LSSVM, one needs to minimize (1) in order to find the
regression estimation:

 
N
i
i
T
i
w b e
J w e w w e
1
2
, , 2
1
2
1
min ( , )  (1)
where the first term is an estimate of the empirical risk,
and the second is the confidence interval for estimation.
There may be overfitting if one just minimizes the
empirical risk. In this case, even if one could minimize
the empirical risk down to zero, the amount of errors on the
test set could be big. In order to avoid overfitting and
generalize well, LSSVM minimizes both of empirical risk
and confidence interval. For LSSVM, the regression
estimation problem is formulated as the optimization
problem:

 
N
i
i
T
i
w b e
J w e w w e
1
2
, , 2
1
2
1
min ( , )  (2)
subject to the equality constraints
y w (x ) b e ,i 1, ,N.
i i
T
i     
 (3)
With the application of the Mercer’s theorem on the
kernel matrix

(as
K x x x xj i j N
T ij  ( i, j )  ( i) ( ), ,
 1,,
), it isn’t
required to compute explicitly the nonlinear mapping
()
as this is done implicitly through the use of positive definite
kernel functions
K(xi,xj )
. Usually, several choices for
K(xi,xj )
are possible.
(1) Linear kernel:
j
T
i j i K(x , x )  x x
,
(2) Polynomial kernel:
d
j
T
i j i K(x , x )  (x x / c 1)
(polynomial of degree d, with c
a tuning parameter);
(3) Gaussian Radial Basis Function (RBF) kernel:
( , ) exp( / 2 )
2
2
K xi
x j   xi  x j 
(

is a tuning
parameter)
The solution of this optimization problem is obtained
by Lagrangian function (4)

  
 

  
N
i
i
y i
b e i
x
Tw i
N
i
i
w e
Tw LS SVM
L
1
( ( ) )
1
2
2
1
2
1
 

(4)
where
 i  R
are the Lagrange multipliers, the
conditions for optimality are given by :

    

   

  

  

i i
T
i
i
LS SVM
i i i
i
LS SVM
N
i
i
LS SVM
N
i
i i
LS SVM
y w x b e
L
e i N
e
L
b
L
w x
w
L
0 ( )
0 , 1, ,
0 0
0 ( )
1
1

 

 

(5)
After elimination of
w
and
i
e
, the following linear
system is obtained:

1
0

   / 
1
T



y
b 0

(6)
where
T
N
y [y , , y ]  1  ,
T
N
[ , ]   1 
The LSSVM regression formulation is then
constructed as follows:

 
N
i
y x iK x xi b
1
( )  ( , )
(7)
where
, b
are the solutions to (6).
As we can see from (7), we can obtain the most
suitable regression function that predicts the supervisor’s
response if only we get
, b
and kernel function K(x,xi).
2.5. Prediction of LSSVM
There are three steps in using LSSVM to predict
(1) Obtain failure data.
(2)Calculate failure rate from
R R i n
n i
n i
R t
i i 0, 1,2, ,
ˆ
,
ˆ
2
1
( )
ˆ
1 0   
 
 
 

(8)
(3) Build LSSVM regression model. In this step, we
need select the optimal kernel function and kernel
parameters. Once the optimal kernel function was selected,
the kernel parameters were obtained using k-fold
cross-validation techniques and grid-search method. In
k-fold cross-validation, first the training set is divided into
k subsets of equal size. Sequentially one subset is tested
using the model trained on the remaining subsets. Thus,
each instance of the whole training set is predicted once so
the cross-validation accuracy is the mean square error
(MSE) of actual output value and predictor value.

 
n
i
yi yi
n
MSE
1
2
( ˆ )
1
(9)
where
y i
represents ture value, and
y i
ˆ
represents
estimate value.
3. Monte Carlo simulation
In this simulation, we use the LSSVM software
package based on Matlab to perform the model training and
testing. A complete sample is generated from the
Logarithmic normal distribution. The sample size is 15 and
the failure times are shown in table 1:
TABLE 1. Failure data obtained from
N Failure time(s) N Failure time(s)
1
92.8841 9
154.4818
2
98.6804 10
156.4329
3
100.1298 11
188.8626
4
134.6422 12
192.9138
5
136.5316 13
199.7002
6
137.1000 14
203.9489
7
147.9441 15
216.1558
8
154.1209 18 154.4818
3.1. Kernel function selection
In order to select the optimal kernel function, we
conducted two different simulation experiments with linear
kernel function and RBF kernel respectively. Simulation 1
had 10 training dataset and 5 testing dataset. Simulation 2
had 5 training dataset and 10 testing dataset.
The parameters
2 
and

of RBF kernel are estimated
by 10-fold cross-validation technique using the following
steps:
1. Set aside 2/3 of the data for the training/validation
set and the remaining 1/3 for testing.
2. Starting from i=0, perform 10-fold cross-validation
on the training/validation data for each (
2  ,

)
combination from the initial candidate tuning sets
 
0
{0.5,5,10,15,25,50,100,250,500}
and
5000,10000}
{0.01,0.05,0.1,0.5,1,5,10,50,100,500,1000, 0 
.
3. Choose optimal (
2  ,

) from the tuning sets
i
and
i
by looking at the best cross-validation
performance for each (
2  ,

) combination.
4. If i=imax (usually imax=3 ), go to step 5; eles i:=i+1,
construct a locally refined grid
i ×
i
around the
optimal parameters (
2  ,

) and go to step 3.
5. Construct the regression LS-SVM using the total
data set for the optimal choice of the tuned parameters
(
2  ,

).
6. Assess the test set accuracy by means of mean
square error (MSE).
The linear kernel parameter is estimated by 10-fold
cross-validation technique using the following steps:
1. Set aside 2/3 of the data for the training/validation
set and the remaining 1/3 for testing.
2. Starting from i=0, perform 10-fold cross-validation
on the training/validation data for each

combination
from the initial candidate tuning set
20000}
{0.5,1,5,10,50,100,500,1000,5000,10000,12000,15000. 0 

3. Choose optimal

from the tuning set
0
by looking
at the best cross-validation performance for each

combination.
4. If i=imax (usually imax=3 ), go to step 5; eles i:=i+1,
construct a locally refined grid
0
around the optimal
parameters

and go to step 3.
5. Construct the regression LS-SVM using the total
data set for the optimal choice of the tuned parameters

.
6. Assess the test set accuracy by means of mean
square error (MSE).
The prediction estimator of testing database of
simulation experiment 1 are shown in Fig. 1. The prediction
estimator of testing database of simulation experiment 2 are
shown in Fig. 2.
FIGURE 1. Prediction value of testing data sets of simulation 1
FIGURE 2. Prediction value of testing data sets of simulation 2
4. Conclusions
Logarithmic normal distribution is often used to
describe the failure characteristic of IC. It is not unusual
to only have a small set of reliability data samples for
microelectronics devices in nano era. Monte Carlo
simulations indicate that LSSVM prediction method is
effective for analyzing small sample reliability data.
Comparing with BP neural network, LSSVM prediction
method has two advantages:
(1) It improves the effectiveness and accuracy of
lifetime prediction method based on LSSVM of small
sample reliability data. And the accuracy of estimation is
higher of LSSVM method than BP neural network method
for small samples. The reason lies in the different
statistical principle between LSSVM and BP neural
network. BP neural network is based on empirical risk
minimization principle. However, LSSVM is based on
structural risk minimization principle, which minimizes
both of empirical risk and confidence interval.
(2) The simulation experiments show that lifetime
prediction method based on LSSVM is suitable for
predicting lifetime of small failure data samples that obey
logarithmic normal distribution. When 10 failure data
samples are used to build LSSVM model, the estimator of
LSSVM prediction method corresponds closely to the
actual value. Even when only 5 failure data samples are
used to build LSSVM model, the estimator of LSSVM
prediction method still corresponds closely to the actual
value.
In conclusion, a method, for life prediction of small
samples data from logarithmic normal distribution, is
developed based on LSSVM. If only an optimal
regression model is trained, the high accurate of estimation
can be obtained even for a small sample data. So the
LSSVM method can be used for life prediction study for
failure data from logarithmic normal distribution.

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