IRAOI JOURNAL OF STATISTICAL SCIENCES

In this paper we have investigated three algorithms. In
the first algorithm we have derived a new optimal step size
gradient algorithm which is preferable over the classical SD
algorithm both in theory and in the real computation. In the
second algorithm we have derived and implemented a new
formula for the non-quadratic model with a new i . In the third
algorithm we have tried to make a new hybrid algorithm between
the above three different step sizes.
Our numerical results are promising in general by
implementing ten non-linear different test functions with
different dimensions.

In this Research, we employ Principal Components manner for
the forecasting in the time series , we are making the forecast to a
data which are used from the researcher ( Ljung,1999). When we
use the principal components,we obtain a good forecasting with
a good statistcal specifics

In this paper we present a technique for computing the
minimum value of an objective function in the frame of gradient
descent methods based on combination of Barzilai and Borwein
approximation of Hessian matrix of objective function and Lipchetz
constant in the gradient flow algorithm which is derived from a
system of ordinary differential equations associated to
unconstrained optimization problem. This algorithm suitable for
large- scale unconstrained optimization problems, computational
results for this algorithm is given and compared with BB method
showing a considerable improvement

The importance of the classification of the satellite images
appeared as one of the important subjects in which the
researchers were interested. The traditional and hybrid methods
were employed in the research.
In the traditional method the decision tree and K-means
clustering algorithm were used, which gave bad and accepted results in classifying satellite images, so the importance of
genetic hybrid algorithm study is concerned with classifying the
satellite images.
Also a hybrid system was suggested between the genetic
algorithm and the k-means clustering algorithm, which gave
good results in classifying the satellite images

This paper deals with the comparison between some linear filter
for time series represented by Weiner filter through
autoregressive model, moving average model and difference
filter. An application for lung cancer Time Series in Mosul city
during (1990-2000) is also considered

This study finds out a new algorithm for scheduling
problem in computer, This algorithm was compared with
HLFET(Highest Level First With Estimated Times Scheduling
Algorithm) and SCFET (Smallest Co-Levels First With
Estimated Times Scheduling Algorithm) scheduling algorithms.
By letting the communication cost be equal to zero, It showes
high efficiency and easy to use.

This research concentrates on the study of genetic
algorithm which is regarded as one of the approaches of modern
artificial intelligence, an algorithm is suggested to transfer a gene
to solve the problems of transportation. The application of this
algorithm to the matters of balanced transportation led to a
number of solutions equal to the number of generation including
the most ideal solution (the least possible total cost). In addition,
a new leap is suggested through the algorithm of genetic
transference whose use led to tangible importance in the results
(reducing the value of the total cost).

In factor analysis, whenever the number of variables (m) is
less than the number of the experimental units (n). The procedure
of R-mode should be applied, if (m) exceed (n) the Q-mode is the
suitable procedure to follow. A shortcut transformation between
the results of the two procedures was established in any
analytical step. Some of the results are: eigen vectors, factor
loadings, factor coefficients and factor scores

This paper studied the relationship for some variables that
direct and indirect effect for the agriculture investment and
agriculture local output for the period (1980-2000), to help us to
identification how this variables affect to the agriculture sector
and what extent responding for this sector to this variables, for
estimation we apply (Full Information Maximum Likelihood)
(FIML) to estimate the parameters by using (SAS) and
(MINITAB) Packages

In this research, we use on ( MSE ) and ( MSFE ) two
criterions to compare between the estimators of Adaptive
Regression model and the estimators of Ordinary and General
Least Squares, we found that, Adaptive Regression estimator is
the best by comparing with estimators because minimum mean
square error.