This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. 相似文献
The wavelet scaling functions of spline wavelets are used to construct the displacement interpolation functions of triangular
and rectangular thin plate elements. The displacement shape functions are then expressed by spline wavelet functions. A spline
wavelet finite element formulation of thin plate bending is developed by using the virtual work principle. Two numerical examples
have shown that the bending deflections and moments of thin plates agree well with those obtained by the differential equations
and conventional elements. It is demonstrated that the current spline wavelet finite element method (FEM) can achieve a high
numerical accuracy and converges fast. The proposed spline wavelet finite element formulation has a wide range of applicability
since it is developed in the same way like conventional displacement-based FEM. 相似文献
In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume that we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given an input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost-weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them on classification performances, but also requires significantly less running time.
Construction solid waste (CSW), an inescapable by-product of the construction and demolition process, was used as main substrate in a four-stage vertical subsurface flow constructed wetland system to improve phosphorus P removal from domestic wastewater. A 'tidal flow' operation was also employed in the treatment system. Under a hydraulic loading rate (HLR) of 0.76 m3/m2 d for 1st and 3rd stage and HLR of 0.04 m3/m2 d for 2nd and 4th stage of the constructed wetland system respectively and tidal flow operation strategy, average removal efficiencies of 99.4% for P, 95.4% for ammoniacal-nitrogen, 56.5% for total nitrogen and 84.5% for total chemical oxygen demand were achieved during the operation period. The CSW-based constructed wetland system presents excellent P removal performance. The adoption of tidal flow strategy creates the aerobic/anoxic condition intermittently in the treatment system. This can achieve better oxygen transfer and hence lead to more complete nitrification and organic matter removal and enhanced denitrification. Overall, the CSW-based tidal flow constructed wetland system holds great promise for enabling high rate removal of P, ammoniacal-nitrogen and organic matter from domestic wastewater, and transforms CSW from a waste into a useful material. 相似文献