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This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification...
This paper presents a novel identification method of induction motor via Dynamic Neural Networks with two-time scales using sliding mode learning. Due to the fast adaptation and superb learning capability, Dynamic Neural Networks with two-time scales using sliding mode learning are used to identify the induction motor including the aspects of fast and slow phenomenon. The sliding mode technique and...
This paper roots in the sub-project “Development of 60 dwt AC Driving Articulated Dump Truck” of National's 11th five-year Scientific and Technical Support Plans project (No.2008BAB32B03). For the vehicle traction Motor that use water cooled stator structure, computational fluid dynamics(CFD) simulation software Fluent is introduced to analyze the flow characteristics and thermal characteristics of...
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