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Analysis of dynamic properties of interactive drive systems is lately the object of interest among researchers from both academic and industry fields. This interest is reflection of mutually contradicting requirements put into the development of current drive systems, mainly the drives itself. On one hand we see the performance and failure-free operation requirements, on the other hand the reduction...
The paper compares global end local approximation methods used in inverse problems. Global approximators are represented by feedforward multilayer neural network (FFNN); local approximators are represented by Locally Weighted Regression (LWR) and Receptive Field Weighted Regression (RFWR).
Q-learning algorithm in its standard form is limited by discrete states and actions. In order to improve quality of the control the algorithm must be modified to enable direct use of continuous variables. One possible way, presented in the paper, is to replace the table, by suitable approximator.
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