UDK 004.032.26
V.O. Lazarev
ADVANTAGES OF THE USE OF ARTIFICIAL NEURAL NETWORKS IN
THE ANALYSIS OF BUSINESS INFORMATION AND DATA-MINING
APPLICATIONS.
Summary. The publication is dedicated to the usefulness of artificial neural networks in
the analysis, forecasting, and support decision making. Considered arguments about the benefits
of artificial neural networks for data-mining applications.
Key words: artificial neural networks, decision support system, forecasting.
Formulation of the problem. Computer Information Systems used in
enterprises have been a number of stages in its development. In the beginning were
the most in demand data collection system, then their formalization and storage. At
the present time any enterprise there is a need analysis of huge volumes of business
information of all kinds. The most pressing is the problem of converting large
amounts of tabular information, typically stored in a database, in a concise set of
rules of business strategy, a clear forecast for the procurement of goods or
promoting optimal management decision. At the moment, most of the sources
define this process as a data-mining (process analytical study of large volumes of
economic data in order to identify specific patterns and systematic relationships
between variables, which can then be applied to new collection of data). For
brevity, we will assume that the data-mining is the process of extracting knowledge
from structured data.
The main methods of production of knowledge is the analysis of hypotheses
and exploratory data analysis. These methods are proven and can be considered
classic, but a significant disadvantage of their use is a focus on determine the
nature of the process of data distribution, and not on the end result of the forecast.
Also we need to look to the rather complex mathematical tools and low parallelism
received, on this basis, algorithms.
A skilled user of the computer information system during data analysis
expects fairly simple and flexible tools that make it easy to add, delete, or modify
rules retrieval. At the same time, users will impose strict requirements to the
automatism and speed the process of working with data. Such rather contradictory,
requirements to the functional information system makes search for new methods
of data-mining.
An alternative description of the methods above data-mining can offer the
use of artificial neural networks.
Analysis of the latest research and publications. Topic various practical
applications of artificial neural networks are widely disclosed in the domestic and
foreign literature. The most well-known authors: Mohamad H.Hassoun,
A.A.Ezhov, S.A.Shumsky, S.Haykin, L.G.Komartsova, A.V.Maksimov,
A.I.Galushkin, R.Duda, P.Hart, V.A.Golovko, G.E.Yahyaeva, D.A.Tarhov,
V.V.Kruglov, M.I.Dli, R.Yu.Golunov.
In research of neural networks with general regression and probabilistic
neural networks: Burrascano, P., Schieler, H., Hartmann, U., Caudill, M. P.Tavan,
H.Grubmuller, H.Kuhnel.
Publications on the practical application of artificial neural networks can be
found under the authorship: RCFrye, J.Vac.Sci.Technol, KDCummings,
EARietman, CMBishop, B.Denby, S.V.Klimenko etc.
March 20, 2012 was held X All-Russian Scientific Conference
"Neurocomputers and their application." The conference was devoted to topical
issues of neural networks and other learning institutions to solve problems in the
natural and engineering sciences and humanities. Purpose of the conference unification of professionals working in various fields of application of neural
network algorithms, discussion and generalization of their theoretical and practical
research, the definition of prospects of the educational structure.
The purpose of this publication is to justify the application of the
methodology of artificial neural networks in computer information systems, data
mining (data-mining) and the support of decision-making.
The presentation of the basic material of the research. Artificial neural
network is a system of connected and interacting artificial neurons [1; p.9]. A
mathematical model of artificial neuron was proposed by W. McCulloch and W.
Pitts, along with a model network of these neurons. The authors showed that the
network on such elements can perform numerical and logical operations.
Practically network was implemented by Frank Rosenblatt in 1958 as a computer
program, and later as an electronic device - Perceptron.
1 - neurons transmit
signal;
2 - adder weights
incoming signals;
3 - transfer function;
4 - neurons of the
received signal;
Pic. 1. Diagram of an artificial neuron work.
By means of an artificial neural simple processors, who are responsible for
reception, processing, and forwarding (Pic.1). Artificial neuron can have a large
number of input connections for which it receives signals from other neurons [2;
p.71]. Artificial neuron can be represented as a function (transfer function) of one
argument, which is the combination of the input signals. The result of transfer
function is transmitted to the only way out of artificial neuron connected to the
inputs of other neurons in the network or external devices.
The range of values of triggering artificial neuron is usually defined as [0,1]
or [-1,1]. By type of transfer function mainly neurons can be divided in:
1. Step transfer function - the output y of this transfer function is binary,
depending on whether the input meets a specified threshold. As long as the
weighted signal at the input of the neuron reaches a certain level - the output is
zero. As soon as the input signal exceeds the level of the neuron - output signal
abruptly changes by one.
(1)
2. Linear transfer function - The output of the neuron is linearly related with
the weighted sum of signals at its input.
(2)
3. Sigmoid transfer function - One of the most frequently used, for the
moment, the type of transfer functions. Introduction of sigmoidal functions such as
is caused by limitations of neural networks with threshold activation function of
neurons - with such activation function of any of the outputs of the network is
either zero or one, which restricts the use of networks is not in classification
problems. The use of sigmoidal functions allowed to pass from neuron to the
binary outputs analog.
(3)
There are also other types of transfer functions, but they are beyond the
scope of this paper.
Thus artificial neuron receives input signals of one or more neurons,
characterized by a marked source and weight then runs over the resulting array
signal transfer function and send the results to one or more neurons, indicating the
source and weight as a result of the transfer function [3; p.25]. From the above it
can be seen that the artificial neural networks have the highest degree of scalability
and flexibility of finite network model.
In general, the artificial neural network consists of three parts (Pic.2):
1. Input layer - accepts data from external sources, and provides them with
filtering.
2. Hidden layer - it is the main data processing, hidden layers can be quite a
lot of it depends on the complexity of the network problems.
3. Output layer - contains the result of data processing in an artificial neural
network is used for the final presentation and transmission to an external device.
Pic. 2. The scheme of an artificial neural network.
To perform its functions, artificial neural network must learn (fitting) [4;
p.64].
Fitting network is to tweak the balance transferred artificial neural signals.
The process of learning network can be implemented by two main methods:
supervised learning and unsupervised learning. In the case of training a neural
network with a teacher, a special training set is input to the network, then the
network is configured in such a way to display the expected results at the output.
Defined above basic concepts of artificial neural networks, we turn to the
use of their methodology, business intelligence applications, economic forecasting,
support decision-making and data-mining [5; p.89].
One of the main advantages of using artificial neural networks for datamining applications is a fundamental change in the behavior of a computer
information system. The classic examples of systems in the first stage, the input
data in the store (database), and then you perform the conversion, screening,
clustering and data analysis. These operations typically use multiple reads and
writes data on the media, which often reduces performance and increases wear.
Application of artificial neural network in the first stages of the data allows us to
solve problems filtering, clustering, classification and ranking of incoming data in
a single process, which is ideally used parallelism that our time is one of the
requirements imposed by the development of the hardware. In the case of artificial
neural networks, information is saved to the database is more than ready for further
use, reduced the proportion of "garbage data" and unprofitable data [6; p.47].
Thus holding data "gauntlet" of neural networks, with the right choice of
topology and settings, can reduce the amount of information due to more accurate
screening, perform clustering, just data entry, increase the performance of a
computer information system through using parallel computing, and reduce the
wear of storage devices.
The main instruments of data-mining applications are clustering,
classification and ranking. When you use these applications to solve business
problems in the modern model of a competitive market economy, an unavoidable
part of changing the values of criteria for analyzing the data, and often the
appearance of new criteria, the gradual demise of the old. In classical computer
information systems often narrowly defined ranges of the criteria and the ability to
create new boast, it is absolutely not many programs. Is often the case when people
are faced with the need to use external programs (such as spreadsheets) for more
flexible handling of data, but it entails trouble exports and lack of a single logic.
Use artificial neural networks in applications data-mining, if done right,
gives a unique designer of the new criteria for data extraction tool [7; p.15].
Practical implementation of this functionality is highly dependent on the network
topology. For example using a multilayer perceptron obtain new criteria can be
achieved by introducing an additional hidden layer neurons. In other words, when
developing applications data-mining, using artificial neural networks to introduce
appropriate tools allow experts and advanced users, within certain limits, to
upgrade the network architecture in response to changing business requirements [8;
p.65]. Such an approach would extend the life cycle of your application. Another
positive effect it is to create a community around a similar application
configurators and implementers, will have at its disposal an effective platform
data-mining, the effect of this effect can be illustrated, for example, a successful
software product 1C: Enterprise and the like.
Conclusions. The technology is often used solutions peeped in nature.
Artificial neural networks are no exception, they copy the structure of the nerve
cells of humans. Despite the fact that the idea of artificial neural networks for a
long time there was a practical application of this approach reveal their capabilities
until now. Development of processors in the direction of increasing the number of
cores (execution units) determines the choice of methods and algorithms with a
high level of parallel processing. From this perspective, artificial neural networks
are ideal because they are arrays of simple process (artificial neurons) are running
at the same time (for each layer of the network).
The program uses artificial neural networks, have a high degree of
modularity, which is a prerequisite of any modern application. Wide scalability
caused by the fact that the introduction of new criteria and conditions data is
sufficient to add a new hidden layer of artificial neurons, with explicit restrictions
on the number of layers does not exist. You can change a wide range of
functionality of an artificial neural network retraining it, ie creating new learning
samples.
Finally, the use of artificial neural networks, gets along well with objectoriented programming [9; p.31], which is the modern standard of development.
Each type of artificial neuron is the use of which is scheduled for danno network
can determine the appropriate class, in this case, specific neurons will be instances
of the class, and the synapses - the class interface.
All of the above suggest a high feasibility of using artificial neural networks
in applications associated to the handling large volume of business information.
References
1.
Hassoun Mohamad H. Fundamentals of Artificial Neural Networks /
Hassoun Mohamad H. - Cambridge, Massachusetts : MIT Press, 1995. - 354c.
2.
Ежов А.А. Нейрокомпьютинг и его применения в экономике и
бизнесе / А.А. Ежов, С.А. Шумский. - М. : МИФИ, 1998. - 460c.
3.
Хайкин С. Нейронные сети : полный курс / С. Хайкин. - [2-е изд.].
- М. : Вильямс, 2006. – 600 c.
4.
Комарцова Л.Г. Нейрокомпьютеры / Л.Г. Комарцова, А.В.
Максимов. - М. : Изд-во МГТУ им.Баумана, 2004. – 250 c.
5.
Галушкин А.И. Нейронные сети. Основы теории. / А.И.
Галушкин. - М. : Горячая линия - Телеком, 2010. - 320c.
6.
Kosko B. Neural Networks and Fuzzy Systems. A Dynamical Systems
Approach to Machine Intelligence / Kosko B. - Prentice Hall : Englewood Cliffs,
1992. - 400c.
7.
Головко В.А. Нейронные сети: обучение, организация и
применение / В.А. Головко. - М. : ИПРЖР, 2001 – 280 c.
8.
Тархов Д.А. Нейронные сети. Модели и алгоритмы.
(Справочник.) / Д.А. Тархов. - М. : Радиотехника, 2005 – 370 c.
9.
Круглов В.В. Нечёткая логика и искусственные нейронные сети /
В.В. Круглов, М.И. Дли, Р.Ю. Голунов. - М. : Физматлит, 2001. – 415 с.