Abstract
The paper considers the use of artificial neural networks in order to synthesize intelligent systems governed by a synergetic control law. It has been shown that so far all the studied objects and, therefore, control laws, have been considered linear, or have been treated to reduce them to such, thereby compromising their certain features. However, as evidenced by practice, actual objects are mostly nonlinear. Consideration of such objects with an attempt of their linearization leads to that the important characteristics of the entire process are lost. A greenhouse complex is mostly composed of such nonlinear objects of control. A greenhouse, as well as each process separately, are not exception.We have proposed basic provisions to the synergistic approach related to the systems synthesis task. The synergistic synthesis of control law has been shown for a greenhouse complex under conditions of non-controlling changes in the technological parameters and external disturbances. The applied mathematical apparatus of fuzzy logic enables the implementation of fuzzy control. It manifests itself particularly positively under conditions when the processes are difficult to analyze by using conventional quantitative methods. As well as when the acquired information about the object is substandard, inaccurate, or ambiguous. This is exactly the type of information received for analysis and its subsequent use when growing vegetables at greenhouse complexes. We have proposed an algorithm to synthesize a neuro-network controller for a greenhouse complex based on the predefined synergetic control law. The algorithm is based on the performance of the synergistic controller that simulates values for temperature and humidity from an artificial neural network following our training it. A feature of the proposed integrated approach to the synthesis of an intelligent control system for a greenhouse complex is a combination of the principle of unification of the processes of self-organization and training a neural network at a preliminary stage. Such a combination ensures further stable functioning of the system aimed to intelligently control the cultivation of vegetable produce.
Highlights
A distinctive feature in managing current neural networks is the presence of an appropriate set of direct and inverse relationships
Intelligent management of a greenhouse complex must take into consideration that the application of intelligent control systems is based on the principles listed below, as confirmed by studies that addressed the neuro-fuzzy systems to control energy consumption at greenhouses and biotechnological facilities [1, 2]:
– maintaining operation at a disruption in communi cation or at a loss of controlling influence from the higher levels of hierarchy of the governing structure. By accounting for these principles, the systems of this kind could be synthesized by achieving the combination of processes of self-organization and management, namely, through using, in order to synthesize intelligent control systems, the synergistic approach
Summary
A distinctive feature in managing current neural networks is the presence of an appropriate set of direct and inverse relationships. – maintaining operation at a disruption in communi cation or at a loss of controlling influence from the higher levels of hierarchy of the governing structure By accounting for these principles, the systems of this kind could be synthesized by achieving the combination of processes of self-organization and management, namely, through using, in order to synthesize intelligent control systems, the synergistic approach. External influences are represented as partial solutions to some additional differential equations that describe an information model, thereby exercising their “immersion” into the general structure of the extended system [3] In this case, an important issue for the synthesized control system is the robustness of its functioning. This indicates the relevance of our research aimed at intelligent management of a greenhouse complex using the synergistic approach and artificial neural networks
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