Research Topic

Hybrid Intelligent Algorithms Based Learning, Optimization, and Application to Autonomic Control Systems

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With the rapid rise of artificial intelligence, a large amount of intelligent techniques, including neural networks, fuzzy logics, genetic algorithms, etc., have been broadly applied to various fields in reality, such as chemical process, robotics, mechanical engineering, etc. In the biological system, the ...

With the rapid rise of artificial intelligence, a large amount of intelligent techniques, including neural networks, fuzzy logics, genetic algorithms, etc., have been broadly applied to various fields in reality, such as chemical process, robotics, mechanical engineering, etc. In the biological system, the neural networks usually contain a finite set of modes that switch in accordance with internal evolution and external stimulation, and such switching can often be represented as stochastic or even nondeterministic form. Nowadays, endless developments have been appeared in the system and control community on control and filtering of intelligent systems with some hybrid switching characteristics, however, the practical applications in the areas of tele-medicine, disease treatment, and healthcare are of lack based on the existing hybrid intelligent algorithms to a large extent. It is also difficult and challenging to implant these hybrid intelligent algorithms to the process of facilities and equipment research and development.

Recently, the autonomic control has been emerged due to the advent of the era of artificial intelligence, and an ever-increasing demand has been placed by the users in different fields. For instance, the studies on autonomic nervous systems have been attracted by the researchers of autonomic neuroscience, and related autonomic control issues have been investigated preliminarily, such as neuronal control of cardiovascular, digestive, genitourinary and respiratory function, and issues that impact more broadly on the body’s activities, such as neuronal regulation of metabolism, feeding and temperature. It is expected that the advanced intelligent algorithms can be fitted into the learning, optimization and control design to improve the autonomic ability of plants. Also the exploration on the communication mechanism between autonomic systems and other regulatory systems is very welcome with the aid of existing approaches on networked control systems with communication constraints. Besides, the complex dynamic behaviors stemming from the mal-function of internal organs can be fully considered in the mathematical modeling of autonomic nervous systems.

To respond to the above challenges, this Research Topic collects papers that are developing various hybrid intelligent algorithms (e.g., neural networks, fuzzy logic, genetic algorithms) to deal with the modeling, learning and optimization issues for dynamic systems including biological nervous system, and applying these advanced intelligent algorithms to cope with the control issues of dynamic systems with autonomic abilities, such as the biological regulatory system; as well as focusing on the optimization and control of autonomic nervous systems from the perspective of system control. Authors are invited to submit original research, reviews/mini reviews, methods and opinion articles related to, but not solely limited to the aforesaid topics.


Keywords: Artificial intelligence, Intelligent control, Machine learning, Optimization, Autonomic Systems


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