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Focusing on the non-stationary characteristic of the fault signal of subway auxiliary inverter, this paper proposes the method that combines ensemble empirical mode decomposition (EEMD) with genetic algorithm to optimize BP neural network (GABP) to diagnose the fault categories of subway auxiliary inverter. Firstly, this paper extracts feature vectors from the original fault signal by EEMD, then establishes...
In this paper, an adaptive output feedback controller was developed for a class of nonlinear systems with unkonwn parameters, uncertain disturbances and unmeasured states. Firstly estimated states was obtained by introducing an observer. Then a controller was developed based on the dynamic surface control approach. Explosion of terms can be avoided because this method no longer needs the repeated...
Extreme Learning Machine (ELM), recently developed by Huang et al., has been demonstrating an exciting learning algorithm for Single hidden Layer Feedback Neural Networks (SLFN). In this paper, the ELM has been introduced to approximate the unknown functions, which may not be parameterized and so make it impossible to develop an adaptive controller. Besides, the Nussbaum-type gain method is also incorporated...
Changing lane is one of the methods to reach the destination faster and also could bring more highway traffic accidents. This study through the traffic feature recognition, cluster analysis, similarity measurements and estimation, analyzed the vehicle operation parameter before changing lane, proposed a changing lane probability estimating model which combines the SOM (Self-Organization Map) and BP...
A new approach to identification of multi-input multi-output (MIMO) Hammerstein-Wiener system is presented. The output nonlinear block consists of several single-input single-output (SISO) blocks, one of which is dead zone and saturation nonlinearity. The hinging hyperplane (HH) model expresses the character. The MIMO input nonlinear block is described by multi layer feed forward neural networks....
In the paper, a regularized correntropy criterion (RCC) for radial basis function neural network (RBFNN) is proposed. In RCC, the Gaussian kernel function is used to replace the Eculidean norm of the sum-squared-error (SSE) criterion. Replacing SSE by RCC can improve the anti-noise ability of RBFNN. Moreover, the optimal weights and the optimal bias terms can be iteratively obtained by the half-quadratic...
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