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The paper presents aspects of model-free learning control initialization. Model-free learning has several advantages as general purpose approach or adaptive capability. However, practical implementation is not intuitive in each step. Choice of time scale in order to provide the necessary reactivity or level of granularity of states or control actions is not an easy problem. Before it learns environment...
This paper study the multi-objective optimization problem of elevator group control systems by using the Markov Decision Process model. Define the Agent to be the leaner and decision-maker of the MDP model. And then using reinforcement learning Algorithm combined with generic method defines the elements of this model. Moreover we use SARSA(λ) value iteration algorithm which was selected to iterative...
Coordinated control system in power plants is a complicated system with nonlinearity and randomcity. It is difficult to build the nonlinear models by the traditional method, so the whole optimal control for thermal processes is impossible. A kind of method of fuzzy identification based on improved T-S (Takagi-Sugeno) model is proposed in this paper. Firstly, the heuristic information and the multiplex...
Aiming at the complex dynamic feature of large ship, an intelligent control structure based on library-similar knowledge-increasable neural network group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output...
The structure of dynamic neural networks and their on-line learning algorithm are two important factors for NAICSs (nonlinear adaptive inverse control system). Recurrent neural networks are effective identifiers for nonlinear plant. However, they have complex architecture. In order to simplify the structure of dynamic neural networks, this paper proposed an improved DAFNN (dynamic activation function...
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