The motion of state vector of cardiovascular system in females working on Surgut condensate stabilization plant was studied in five-dimensional phase space of states. The parameters xi of cardiovascular system in four groups of women varied within the range of VG - bounding volumes of the phase space of states, which was defined as quasi-attractor. The volumes VG were measured and compared in women of different age, affected and not affected by human-made electromagnetic fields of industrial frequency, which allowed establishing the principal differences in the dynamics of VG. In particular, women, both younger and older age groups underwent stress effect of electromagnetic fields of industrial frequency. This led to breakdown in volume of quasi-attractors in phase plane of vector (x1, x2)T where x1 - duration of cardiointervals, and x2 = dx1/dt - rate of change of x1. In fact, electromagnetic fields became stress agents for human's cardiovascular system in the North of Russian. This is shown in the framework of quasi-attractors for cardio of two groups of women. Comparison of these four groups according to the parameters of quasi-attractors showed differences though statistical differences were not significant on a number of xi parameters of whole the body state vector x (f) of person's organism living in the special northern conditions.
В настоящее время не существует единого определения искусственного интеллекта. Требуется такая классификация задач, которые должны решать системы искусственного интеллекта. В сообщении дана классификация задач при использовании искусственных нейросетей (в виде получения субъективно и объективно новой информации). Показаны преимущества таких нейросетей (неалгоритмизируемые задачи) и показан класс систем (третьего типа — биосистем), которые принципиально не могут изучаться в рамках статистики (и всей науки). Для изучения таких биосистем (с уникальными выборками) предлагается использовать искусственные нейросети, которые решают задачи системного синтеза (отыскание параметров порядка). Сейчас такие задачи решает человек в режиме эвристики, что не моделируется современными системами искусственного интеллекта. Currently, there is no single definition of artificial intelligence. We need a Such categorization of tasks to be solved by artificial intelligence. The paper proposes a task categorization for artificial neural networks (in terms of obtaining subjectively and objectively new information). The advantages of such neural networks (non-algorithmizable problems) are shown, and a class of systems (third type biosystems) which cannot be studied by statistical methods (and all science) is presented. To study such biosystems (with unique samples) it is suggested to use artificial neural networks able to perform system synthesis (search for order parameters). Nowadays such problems are solved by humans through heuristics, and this process cannot be modeled by the existing artificial intelligence systems.
в конце ХХ века нобелевский лауреат В. Л. Гинзбург представил три «великие» проблемы физики, которые имеют прямое отношение к живым системам. После открытия эффекта Еськова–Зинченко эти три проблемы перешли в реальные три проблемы биосистем, которые связаны с особенностями биосистем. Вторая реальная проблема из этого списка связана с потерей однородности групп испытуемых. Это действительно «великая» проблема для всех наук, изучающих живые системы, или системы 3-го типа, по W. Weaver. in the end of the 20th century, V. Ginsburg, a Nobel prize winner, presented three “great” problems of physics directly related to living systems. After the discovery of the Eskov-Zinchenko effect, these three problems became the problems of biosystems. The second problem on the list is the loss of homogeneity in sampling groups. This is a “great” problem for all sciences studying living systems or type 3 systems according to W. Weaver.
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