IRAOI JOURNAL OF STATISTICAL SCIENCES

The Back Propagation Algorithm is used for training feed Forward
Multilayer Neural Networks (FFMNN).But often this algorithm takes
long time to converge since it may fall into local minimu, for this reason
we need a long time to train the network. The suitable choice of the
learning rate helps us to escape from slow convergent for the BP and
reduce the time of learning. In this paper, we derived a new adaptive
learning rate for the BP algorithm, our derivation is based on the Aitkin's
process. The most important distinct feature of our approach is the
computing of the learning rate needs only first order derivatives and is
suitable for large training sets and large networks. Its efficiency is proved
on the standard test functions including heart , XOR and function
approximation problems .

The Canonical Correlations Analysis technique (CCA) was
suggested in the dimensionality reduction images for the multivariate
multisource data applied in remote sensing . These techniques transform
multivariate multiset data into new orthogonal variables called Canonical
Variates (CVs) . This research uses the LANDSAT-5 TM data for the set
of multivariate multispectral correlation at fixed points in time . The
results show maximum similarity for the low- order canonical variates
and minimum similarity for the high- order canonical variates .

The aim of this paper is to propose genetic algorithm to finding the
optimal schedule with minimum makespan for n jobs in flow shop
environment with three machines.
In this pape, a new approach for selection, a new crossover operation
(3PLOX) and a new stopping criteria based on the lower bound of the
makespan (LB) are proposed.Also, a new procedure of calculating the
make span for n jobs at processing by three machine in flow shop
environment is suggested. In order to examine the effectiveness of the
proposed GA, a comparison was made with Johnson's algorithm. After
the application on several problems which generated randomly by
uniform distribution, the results showed that the proposed GA is better
than Johnson's algorithm with rate 94% in finding the optimal sequence
for scheduling jobs which gives optimal makespan.

bstract
In this research the scientific dynamic process is studied concering
water filtering in Mosul city through employing the input and output
variables represented by some tests which were performed on raw water
before the filtering process, to be treated later through the numerous filtering
stages.
The tests involved: the electrical conduction, turbidity, water PH, and
water temperature The same tests were conducted after the filtering process
to guarantee that water is good for human consumption and to guarantee the
validity of these tests as well and also to ensure that these tests meet the Iraqi
Standard specifications before adding the chlorine. Four models were
constructed through this study concerning the dynamic filtering process of
the transfer functions with a single input for the four tests. And then models
were built for the four transfer functions as input multiple series before
filtering. Each of the four tests represented the output series by itself and
consequently four models of transfer functions were obtained with multiple
inputs and a single output variable in order to observe the range of impact
for the output variable with the multiple input variables and using these
models in Forecasting and then comparing the single input models and the
multiple input models by means of using the standards of Forecasting
control accuracy. The results showed that the electrical conduction model
and the multiple input PH value were better than the results of the single
input and the turbidity model of single input was better in comparison with
the results of the multiple input. In addition to that, results showed that the
temperature model for the single and the multiple input are similar.

It is well known that the processes of countries development is
the process of planning and setting future plans. This demands the use of
advanced statistical styles. So , we have conducted a comparison between
three methods,namely;Box Jenkins (ARIMA),and Pattern Modeling
Recognition System (PMRS)which is a modern to know the relation
between the current values and the previous ones and the Artificial Neural
Networks (ANN).These three methods were application on the Iraqi
monetary market for the year 2006.The Results showed superiority of
artificial neural networks through error criteria used.

In this paper, the application of Bootstrap Method in predicting the
results of Skipping and Jumping competitions for men in the next two
sessions of Olympic Games 2012 and 2016.
The researcher tried to use the results achieved by all holders of
Olympic medals (top three) in the competitions of Skipping and Jumping
(wide, triple, high, and Pole vault) in six consecutive Olympic Games Seoul
Olympics (1988) is selected as a starting point since it did not face Olympic
sanctions from some countries that took place in two sessions of Moscow
(1980) and Los Angeles (1984), and here arose the idea to use the method of
bootstrap since the time series is too short and it is not easy to determine its
probability distribution, and therefore it is difficult to use classical methods
in the estimation process as well as the sample size is too small.
This paper will illuminate the way for those interested in the
preparation of the teams participating in the four Skipping and Jumping
competitions for well planning based on solid scientific base of the number
and level of participants in these competitions based on the expected
accomplishments brought through the paper in the two Olympic Games,
where the first one will start within days.

The standard number is considered an important statistical tool to
measure change quantity in economic phenomena through two different
periods or places ; the first is named the basis period and the second is the
comparison period (or basis place , comparison place ) . The optimal
standard number is that number which performs the two tests of
chronological reflex in the basis and the reflex in the agent . In this
research a genetic algorithm was created to test the standard numbers
which were programmed by MATLAB9b language which saved a lot of
time and efforts done annually to test the optimum of these numbers.

The purpose of this system is to recognize aperson`s using face
image, and the uses of this system at airports and other offices which use
surveillance cameras that take pictures of views and different angles and
there are qualities in every person face which do not change and are not
affected by the way the picture is taken. In this paper, the remittance waves (Wavelet)are used in order to withdraw more accurate details of
the image, and then features were extracted based on the seven
resolution and the four statistical properties (location measurement mean,
standard deviation and skewness and kurtosis), which address the
problems of image capture by the surveillance cameras.
This system has been in the database configuration to many
people's each person has seven different images in order to configure a
database of training. I have been using Elman neural network as a tool to
identify people, were trained network (Elman) on the line ratio(0.001)and
the number of training courses(1079)and by know(92%). And used the
language of MATLAB R2010a in this system with a database of the type
of Microsoft Excel.