The goal of the research in the PhD dissertation is control of dynamical systems by using new types of orthogonal endocrine neural networks, in order to improve their performances. Standard artificial neural networks are described, as well as their historical development and basic types of learning algorithms. Further, possibilities for neural networks applicability within control logic of dynamical systems are presented, as well as the current state of the art of orthogonal and endocrine neural networks. Performance improvement of the laboratory model of a servo system by using a standard neural network with the backpropagation type of learning is analyzed. In addition, a method for selection and optimization of training data, as an efficient way of information preprocessing for the purpose of improving performances of a neural network, is presented. A detailed description of orthogonal functions and implementation methods of endocrine factors inside standard neural networks are provided. By implementation of orthogonal activation functions of neurons, verification of their applicability in control of dynamical systems was performed. The laboratory model of the magnetic levitation system was used to test the designed orthogonal neural network. Furthermore, the endocrine orthogonal neural network based on the biological processes of excitation and inhibition is designed. Network performance checkup is performed by testing its predictive abilities when working with time series data. Final dissertation researches refer to development of hybrid systems. The implemented adaptive endocrine neuro-fuzzy hybrid system is tested through modeling of a laboratory servo system. Other hybrid structure, based on a combination of an orthogonal endocrine neural network and an orthogonal endocrine neuro-fuzzy hybrid system, is designed with the aim to form symbiosis of the positive characteristics of the individual networks. Verification of this structure was performed by using it for PID controller parameters adjustments.
