System Adaptive

An adaptive system is a system that automatically changes its algorithms and structure to achieve optimal performance under changing conditions. Such systems can be self-tuning, self-learning or self-organizing.

A self-tuning system is a system that can automatically change its parameters and settings to improve its performance. For example, an automatic temperature control system can independently adjust the operating parameters of the air conditioner depending on the outside air temperature.

A self-learning system is a system that is capable of automatically improving its skills and knowledge based on experience and data. Such a system can learn from previous decisions and data to make better decisions in the future.

The adaptive system can be used in various fields, such as production management, transport management, energy management, etc. For example, a transport management system can automatically adjust the speed of a vehicle depending on road conditions and weather conditions.

Living organisms are also adaptive systems. They can change their physiological parameters and structures to adapt to changing environmental conditions. For example, changes in skin color in animals depending on the time of year or changes in the shape of plant leaves depending on changes in light conditions.

Thus, an adaptive system is an important tool for managing complex systems and processes in changing conditions. It allows you to improve the operation of the system and increase its efficiency.

Adaptability is the ability of a biological system to adapt to changes in environmental conditions in accordance with the needs and characteristics of the life of an individual or population. The adaptation is realized on the basis of a variety of physiological and behavioral reactions. Adaptation is based on a restructuring of the body’s activity, a change in its physiological state; it involves changes in organs and systems, their structure and functions and is carried out under the influence of needs.

An adaptive system always strives for some kind of optimum. No system, even the simplest one, can have absolute perfection, because there will always be an even more perfect structure. Therefore, some experts believe that the principle of adaptability is aimed at complicating the structure or adding another additional system.

The amount of information in a system can only increase if the system's behavior worsens. This is ensured by the presence of structural and functional “memory”. When the values ​​of the input parameters change, the optimal values ​​of the output parameters also undergo changes: a switch to a different operating algorithm occurs, depending on their values. This capability of adaptive systems can be seen in the design of process control systems. Optimization procedures, determining optimal control actions and monitoring the optimal process most often should be computational algorithms implemented through software. Often the input parameters of systems and the requirements for optimal values ​​of output parameters change, i.e. changes in the external environment occur. Changes in process requirements may manifest themselves, for example, in a reduction in the cost of products or production time while maintaining high quality. The importance of management tasks in this case does not decrease; rather, on the contrary, although the parameters of the task have changed. The possibility of varying the optimality criterion by directly comparing the values ​​of the variable components is obvious. Probably, as a result of this, when new management models arise, the new model should be compared with the base one - the management model that is optimal according to the initial criterion (criteria). Thus, control models with a large number of variables, the new optimality criteria of which satisfy the conditions specified for the original set of criteria, will be included in the class of general basic models. However, it should be noted that in this case the control problems of slightly interconnected subsystems are solved independently. That is, this situation is unacceptable for systems located at short distances from each other due to electromagnetic interference, since noise occurs in the operation of the system. Currently, the creation of such subsystems is becoming impossible.