Forecasting methods of intelligent systems technical condition analysis

Abstract

Modern information processing and control systems reached the level that makes it possible to use detailed information on device and human nervous system functioning. Using a cognitive psychological approach in intelligent systems development, allowed them to imitate human nervous system functions. The development of increasingly powerful intelligent information and control systems with learning and self-learning, including information and measuring systems, is expected as a result.
 They will have enhanced cognitive capabilities due to the stimulation of cognitive functions and processes responsible for perception, learning, thinking, and consciousness in the human nervous system.
 Development of this field allows to develop intelligent systems with thinking and behaviour analysis elements. Adding some creative possibilities, for example, related to automatic hypotheses and models creation and self-learning for new task solving, allows to improve the efficiency of intelligent systems.
 Due to this new approaches to the artificial brain and the artificial nervous system of robots development, which relate not only to artificial intelligence but also to its development in the form of an artificial mind.
 Thus, the main scientific and development task for models, methods, methodics, and algorithms for intelligent systems technical condition forecasting based on soft computing is important and relevant for science and practice.
 Intelligent technologies allowing to develop useful intelligent systems are continuously being improved. Currently, quite powerful tools for implementing the technology of expert systems, fuzzy logic, neural network systems, and multi-agent systems technology are used. They are rapidly improved by adding software packages and hardware tools.
 New technologies are being developed in the so-called neuromorphic systems field that models some brain structures and in the parallel computing and quantum computers field. These technologies aim to raise significantly the intelligence level of systems in the future.