The results of the relationship between psychological signs and the assessment of their signi cance in the situation of student adaptation to new socio-cultural conditions using the model of the neural network of ART 2 are presented.The external and internal factors that in uence the adaptation of students to the new sociocultural environment are determined. The structure of the neural network model and its learning algorithm are developed. The standardization of scales made it easier to process the results. Using the software product of ART 2-selfregulation, a model of neural networks was created on the factors of adaptation of students to the training group and educational activities. On the basis of the peak indicators of the neural network, four clusters were identi ed that allow students to conduct a typology of regulatory and personal indicators of adaptation processes. Cross-cultural characteristics of representatives of each cluster are established. Thus, the "Initiative" cluster included indicators with peak values: "entry into social contact" (0,253), " exibility" (0,224), "seeking social support" (0,213), "aggressive actions" (0,157), "modeling" (0,106). The main group of students demonstrating the patterns of adaptive behavior are students from Tajikistan. In the cluster "Inert" included indicators: "assertive actions" (0,264), "cautious actions" (0,190), "programming" (0,184), "avoidance" (0,183), "indirect actions" (0,158). The peak values of the "Stereotyped" cluster received scales: "impulsive actions" (0,260), "faults" (0,168), "antisocial actions" (0,166), "perceived hostility" (0,152), "evaluation of results" (0.130). The cluster of the model, called "Closed", combined indicators with peak values: "total level of acculturation stress" (0.169), "perceived discrimination" (0,127), "cultural shock" (0,161) "nonspeci c problems" (0,145), "separation" (0,172). The prospects of using an arti cial neural network in Copyright c by the paper's authors. Copying permitted for private and academic purposes.
interdisciplinary projects are shown. 1
Introduction Intellectual data analysis with the use of neural networks has recently been increasingly used in psychological research, since it helps successfully solve complex problems with a large number of variables and data. Neural networks of the adaptive resonant theory (ART) reveal new input information and, to a certain extent, solve the contradictory problems of sensitivity (plasticity) and the preservation of the previously obtained information (stability). The choice of the task solved with the help of a neural network is determined by the way the network works and the way it is trained. At work, the neural network takes the values of the input variables and outputs the value of the winner's cluster. Thus, the network can be used in a situation where there is a certain array of known information, and it is necessary to obtain from it some information that is not yet known. The neural network is used when the exact type of connections between the input data is unknown, - if possible, a relationship between the input data is linearly constructed, then the connection could be modeled directly without using the art neural network model. Another important feature of neural networks is that the dependence between input and output is in the process of learning the network. Two types of algorithms are used for training neural networks (di erent types of networks use di erent types of training): managed ("training with a teacher") and not managed ("without a teacher").
Despite the existence of a relatively large number of variants of the architectures of arti cial neural networks of adaptive resonance, there are two main ones: ART-1 (for ART, AdaptiveResonanceTheory) for clustering, storage and identi cation of images in the form of binary signals; ART-2 - for clustering, storage and identi cation of images presented in the form of binary signals, and in the form of analog signals, including using both types of signals in one structure.
Despite the existence of a relatively large number of variants of the architectures of arti cial neural networks of adaptive resonance, there are two main ones: ART-1 (for ART, AdaptiveResonanceTheory) for clustering, storage and identi cation of images in the form of binary signals; ART-2 - for clustering, storage and identi cation of images presented in the form of binary signals, and in the form of analog signals, including using both types of signals in one structure.
In our study, for computer processing of psychodiagnostic data, analysis of interrelations between psychological signs and evaluation of their signi cance, we used the model of neural network ART 2 1. 1.1
The purpose of this work is the development of a neural network model capable of functionally describing typologies of students on regulatory and personal indicators of adaptation to a new socio-cultural environment. To achieve this goal, it is necessary to solve the following tasks: -to analyze the process of adaptation of university students in the context of adaptation to training activities and to the training group; -Identify external and internal factors that a ect the adaptation of students to a new sociocultural environment; -choose the methods that determine the regulatory and personal characteristics of students; -to develop the structure of the neural network model and the algorithm of its training; -to train an arti cial neural network; -identify the patterns between the weights of the neural network and the -indicators of student adaptation; -identify the types of adaptation of students on regulatory and personal indicators, taking into account cross-cultural characteristics. 1.2
The research hypothesis is based on the assumption that an arti cial neural network trained without a teacher is able to detect patterns in the training sample and to cluster the input sequence; Arti cial neural network type ART-2 uses the distance between the input vectors to separate them into di erent clusters. The choice of this structure is argued by its simplicity in the analysis of the already trained arti cial neural network ART-2.
So, since this arti cial neural network consists of two layers (input and output), as well as weights a ecting each parameter of the input vector. Having quantitative indicators of weights of concrete input values it is possible to calculate values that in uence the forecasting of respondents' behavior in the context of psychological research The empirical sample of the study included 233 students from North-Caucasian Federal University (CKFU) aged 18 to 25 from Uzbekistan (U) (65 people), Tajikistan (T) (22 people), South Africa (South Africa) (26 people), Iraq (I) (15 people), Angola (A) (24 people), nonresident students from Russia (RF) (81 people). 1.3
Methods of research. A number of psychological methods were selected: the questionnaire "Style of selfregulation of behavior" (SMPM) [Mor04]; ve factorial personality questionnaire (LPO) [Rem11]; method of diagnosis of motivation (MDM) educational activity of an individual in adolescence [Sol07]; the scale of acculturation stress (AS) [San94]; a questionnaire for the measurement of an acculturation unit, developed (adapted) by the method of J. Berry (AU)[San94]; a questionnaire on the identi cation of preferred strategies for overcoming stressful situations (SACS) (S. Hobfoll) allows to analyze models of overcoming behavior [Vod03]; a methodology for studying the adaptation of students (MIAS) in the university [Dubovitskaya TD, Krylova A.V. 2010].
The processing of data obtained using a package of psychological techniques, occurred in several stages. At the rst stage, the data was standardized, each scale was assigned a sequence number to facilitate processing and clarity of the results obtained. At the second stage, the obtained data were processed using the computer program "ART-2 self-regulation", which allowed to train the arti cial neural network ART-2 for constructing the model of adaptation of students to higher education.fig2.eps 1.4
In the model obtained, four clusters were identi ed, which allow us to determine the types of students according to the regulatory and personal indicators of the adaptation processes. We considered only peak values of the neural network as the most signi cant in each cluster.
The rst cluster was named "Initiative", its peak values are: "entry into social contact" SACS (0,253), " exibility" JPKF (0,224), "search for social support" SACS (0,213), "aggressive actions" SACS (0,157) , "Modeling" of the JPKF (0.106). Respondents of this type are active for inclusion in the student group, initiative in communicating and developing techniques for adapting to learning activities in the new environment. Di erences in the level of adaptation of respondents are explained by the distance between the student's native culture and the host culture (Table 1).
The peak values of the second, "inert" cluster are: "assertive actions" SACS (0.264), "cautious actions" SACS (0.190), "programming" SMF (0.184), "avoiding" SACS (0.183), "indirect actions" SACS (0.158). Respondents of this type tend to look at the environment for a long time, carefully build the program of behavior, focusing on their own interests. In this cluster, the proportion of adapted students is much higher (Table 2).
The peak values of the third, "Stereotyped" cluster are: "impulsive actions" SACS (0,260), "fault" AU (0,168), "antisocial actions" SACS (0,166), "perceived hostility" AU (0,152), "evaluation of results" JPKF (0,130). Respondents of this type are suspicious of others, critically assess the requirements of the new environment. This adaptation strategy is not e ective (Table 3).
The peak values of the Closed Cluster are: "the general level of acculturation stress" AU (0.169), "perceived discrimination" AC (0.127), "cultural shock" AU (0.161) "nonspeci c problems" AS (0.145), AS "separation" ( 0.172). Respondents of this type experience the greatest problems with adaptation. Being determined to interact with the host culture, they painfully experience the di erence between the environment and the familiar characteristics of the cultural environment (Table 4).// 1.5
The use of an arti cial neural network in the processing of data sets of psychological research makes it possible to construct predictions of the behavior of respondents in the conditions under study. The use of arti cial neural network type ART-2 in the course of studying the adaptation of students to the new learning conditions made it possible to identify 4 regulatory-personal types, which con rms the prospects of using an arti cial neural network in interdisciplinary projects. [Vod03] N.E. Vodopyanova ,E.S. Starchenkova "Strategies and models of overcoming be-havior Workshop on the psychology of management and professional activity, 311 - 321, St. Petersburg 2003.
"Style of self-regulation of behavior" (SMPM): Management.
Kogito-Center, [Rem11] A.N. Rementsov , A.A. Kazantseva Sociocultural Aspects of Adaptation of Foreign Students in Russian
Higher Schools. Alma mater: Bulletin of Higher Education 7. P. 10 - 14, 2011. [Rom07] N.A. Romusik A technique for diagnosing the motivation of the educational activi-ty of an individual in adolescence. Sociocultural Aspects of Adaptation of Foreign Students in Russian Higher Schools. Alma mater: Bulletin of Higher Education 7. P. 10 - 14, 2011. [Khr03] A.B. Khromov Five-factor questionnaire personality: Teaching-methodical manu-al Kurgan Workshop on the psychology of management and professional activity,Publishing house Kurgan state. University 311 - 321, St. Petersburg 2000.