These theses contains realization of a typical technological beneficiation complex for automation of control processes (in the context of beneficiation of iron ore magnetite quartzites). The hierarchy scheme of intelligence control system for such complex combining principles of neurocontrol, classification and optimal control has been shown. Results of computer modeling of classification optim...

Reinforcement learning (RL) is a model-free tuning and adaptation method for control of dynamic systems. Contrary to supervised learning, based usually on gradient descent techniques, RL does not require any model or sensitivity function of the process. Hence, RL can be applied to systems that are poorly understood, uncertain, nonlinear or for other reasons untractable with conventional methods...

This report constitutes an expanded version of a presentation given by the author at the 1993 European Control Conference (short course on “Neural Nets for Control”). The first part places neurocontrol techniques in a general learning control framework. The second part of the report, which is essentially independent of the first, briefly surveys several basic theoretical results regarding neura...

Neurocontrol is a crucial area of fundamental research within the neural network eld. Adaptive Heuristic Critic learning is a key algorithm for real time adaptation in neurocontrollers. In this paper we present how an unsupervised neural network model with adaptable structure can be used to speed-up Adaptive Heuristic Critic learning, its FPGA design , and how it adapts the neurocontroller to t...