Скачать с ютуб Dendritic Artificial Immune Network Model for Computing в хорошем качестве

Dendritic Artificial Immune Network Model for Computing

Mykola Korablyov (1), Oleksandr Fomichov (1), Matvii Ushakov (1), Mykyta Khudolei (2) 1 - Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine 2 - Taras Shevchenko National University, Kyiv, 01033, Ukraine Today, a large number of methods and models of intelligent information processing have been developed, among which artificial immune systems (AIS) can be distinguished, which are used to solve various practical problems. At the same time, existing models of AIS have a number of disadvantages, the main of which are low productivity and relatively low accuracy. Therefore, the work sets out the task of building such an AIS model that would provide better calculation characteristics both in terms of speed and accuracy. A new model of an AIS in the form of a dendritic artificial immune network (DaiNET) is proposed, which is built using graph theory and allows increased speed, ensures acceptable accuracy of results, and reduces the complexity of the antibody network formation process. The formation of the dendritic structure of the immune network is considered an example of solving the object clustering problem, which is one of the main areas of the practical application of AIS. It is proposed to form a connected graph of antibodies, in which the affinity of antibodies is used as a measure that determines the strength of the connection between antibodies in the immune network. The determination belonging to the clusters of antibodies of the immune network is based on the values of their avidity for each of the clusters, which are based on the affinity between immune objects. The general scheme of the data clustering algorithm based on the DaiNET immune model is considered, which is represented by the sequential execution of the stages of preparation, formation of a K-connected immune network, and network interaction. The peculiarities of the work of immune operators of the DaiNET model are considered. The results of a comparative analysis of the proposed DaiNET immune model with existing immune models and other clustering methods on different data sets are presented, which showed that it outperforms other immune models both in terms of speed and accuracy of object grouping.