The dissertation is devoted to solving the problem of increasing the efficiency of the components of neural network control systems, which allow to synthesize neural network control systems that function and adapt in real time taking into account the specifics of control tasks. ANN can be used in facilities such as robotics, unmanned aerial vehicle control, vehicle control, pattern recognition, analysis and decision making in the Internet of Things, spacecraft control, military equipment and many other applications of modern technolo-gies. In these systems, neural networks can be used to identify objects, predict the state of objects, recognize, cluster, classify, analyze large amounts of data coming at high speed from a large number of devices and sensors, and more. ANN can be used to build control and correction devices, reference, adaptive, nominal and inverse-dynamic models of ob-jects, based on which the study of objects, analysis of the impact of perturbations acting on the object, determining the optimal control law, search or calculation of the optimal program to change the control effect when changing the values of the parameters of the object and the characteristics of the input data. Thus, the scientific and technical task of increasing the efficiency of hardware implementation of neural network components of control systems for dynamic objects, provid-ing adaptation and self-tuning of control systems in real time is relevant. The purpose of the dissertation is to increase the efficiency of neural network control systems by creating high-speed components that allow you to implement the functions of identification, adaptation and control of dynamic objects in real time. The results of the research are presented in four sections of the dissertation. In dissertation analyzes the areas of application of ANN hardware, the main struc-tures of neural network control systems for dynamic objects. Based on the analysis, it was found that neural network control systems consist of direct and inverse models of the con-trol object and the component of their adaptation. Based on the ANN analysis, it has been established that several dozen types of ANN have been developed and researched to date, but the main, fundamentally different types are three types of networks: RBF-networks, Hopfield dynamic networks and direct distri-bution networks. Due to the properties ANN, they can be used for further design on their basis of direct and inverse models of the control object. The existing methods and algorithms of ANN learning are analyzed. Based on the analysis, it is established that the use of a genetic algorithm for training the neural network components of the CS is the most optimal for hardware implementation. The analysis of the current state of software, hardware and hardware-software means of realization of ANN is carried out. As a result of the analysis it was established that the means of implementation of neural network control systems should be focused on widespread use in industrial conditions, be universal and flexible, function and learn in real time, be simple and cheap, so the most promising tools can be considered FPGA. The review of the main works on realization of ANN by means of FPGA is carried out. In dissertation proposes a method for designing nonlinear activation functions of an artificial neuron on an FPGA. Based on the proposed method, algorithms for hardware implementation of an artificial neuron with sigmoidal activation function and a hidden layer neuron of the RBF network with a Gaussian activation function have been developed. A research of implemented artificial neurons and ANN was performed. It is shown that due to the developed method and algorithms significant optimization of the used resource is provided, the speed of calculations of hardware units with ANN and their accuracy in comparison with analogues increases. In manuscript develops technology for construction, research and evaluation of neu-ral network models of multidimensional control objects for their further implementation. A method of designing hardware components, such as direct and inverse model of control ob-ject, neural network control systems, which are basic for structural synthesis of control systems, for calculation of state vectors of object and formation of control function is developed. The method of optimization of ANN weighting factors by means of genetic algo-rithm at realization on FPGA is developed that allows increasing considerably speed of ad-aptation of direct and inverse model of control object. In dissertation implemented and researched on the basis of the developed components: control systems without feedback; feedback control systems; adaptive control sys-tem with direct and inverse models of the control object; adaptive neural network control system with a reference model; a model of the operating neurocontroller of the stabilization system of a moving object on a limited plane is developed.
