As an efficient incremental learning system based on random vector function-link network(RVFLN),broad learning system(BLS)has the characteristics of fast adaptive model structure selection and high precision.However,due to the lack of label data in target classification,the traditional BLS is difficult to improve the classi-fication effect of target domain by using relevant domain knowledge.Therefore,a domain adaptive BLS(DABLS)model based on manifold regularization framework and maximum mean discrepancy(MMD)is developed to achieve cross-domain image classification of target domain under unlabeled condition.Firstly,the feature nodes and en-hancement nodes of BLS are constructed to effectively extract features from the data of source domain and target domain.The manifold regularization framework is used to construct Laplacian matrix in order to explore the mani-fold characteristics of the target domain data and mine the potential information of the target domain data.Then the transfer learning method is used to construct the MMD penalty term between the source domain data and the target domain data to match the projection mean between the source domain and the target domain.The feature nodes,enhancement nodes,MMD penalty term and Laplacian matrix are combined to construct the objective func-tion.Ridge regression analysis is used to solve the objective function to obtain the output coefficients,so as to im-prove the cross-domain classification performance.Finally,a large number of validation and comparative experi-ments are carried out on different image data sets,and the experiment results show that the DABLS can better achieve cross-domain classification on different image data sets,and has strong generalization ability and better sta-bility.
The problem of finite-time global prescribed performance control (GPPC) for vehicular platoon control system (VPCS) with actuator nonlinearities is investigated in this paper. A smooth equivalent transformation is first presented to process both actuator dead-zone and saturation under the same framework. Then, a GPPC method based on improved finite-time performance function (IFnTPF) is proposed, and makes the tracking error tend to the prescribed region within the predefined time, while removes the strict restrictions on the initial conditions of the system. To realize the platoon in given time, an adaptive finite-time sliding mode control scheme is further developed, and both individual vehicle stability and string stability are guaranteed. Numerical simulations demonstrate the validity of the given scheme.
Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste incineration, a heterogeneous feature ensemble modeling method for furnace temperature is proposed in this paper. First, heterogeneous features are generated according to the operation mechanism of the waste incineration process, and the training subset of the furnace temperature- and grate temperature-based model is determined from the historical data of this process. Second, the base model pools of furnace temperature and grate temperature are constructed by a regularized stochastic configuration network, and a set of optimal base models are retained by selective base model technology. Then, a negative correlation learning strategy is employed to establish a simultaneous training ensemble model of furnace temperature, and a regularized stochastic configuration network is used to establish a secondary training ensemble model of furnace temperature. The final output of the furnace temperature is obtained by the average value of the output of the above two ensemble models. Finally, a comparative experiment is carried out using the historical data of a waste incineration plant. The results show that the furnace temperature model established in this paper has advantages in accuracy, generalization ability, stability, and training efficiency. It can be applied to the field of furnace temperature prediction and control in the waste incineration process.
In this work, we tackle the problem of online adaptation for stereo depth estimation, that consists in continuously adapting a deep network to a target video recordedin an environment different from that of the source training set. To address this problem, we propose a novel Online Meta-Learning model with Adaption (OMLA). Our proposal is based on two main contributions. First, to reducethe domain-shift between source and target feature distributions we introduce an online feature alignment procedurederived from Batch Normalization. Second, we devise a meta-learning approach that exploits feature alignment forfaster convergence in an online learning setting. Additionally, we propose a meta-pre-training algorithm in order toobtain initial network weights on the source dataset whichfacilitate adaptation on future data streams. Experimentally, we show that both OMLA and meta-pre-training helpthe model to adapt faster to a new environment. Our proposal is evaluated on the wellestablished KITTI dataset,where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.
基于深度学习的目标检测方法是目前计算机视觉领域的热点,在目标识别、跟踪等领域发挥了重要的作用.随着研究的深入开展,基于深度学习的目标检测方法主要分为有锚框的目标检测方法和无锚框的目标检测方法,其中无锚框的目标检测方法无需预定义大量锚框,具有更低的模型复杂度和更稳定的检测性能,是目前目标检测领域中较前沿的方法.在调研国内外相关文献的基础上,梳理基于无锚框的目标检测方法及各场景下的常用数据集,根据样本分配方式不同,分别从基于关键点组合、中心点回归、Transformer、锚框和无锚框融合等 4 个方面进行整体结构分析和总结,并结合COCO(Common objects in context)数据集上的性能指标进一步对比.在此基础上,介绍了无锚框目标检测方法在重叠目标、小目标和旋转目标等复杂场景情况下的应用,聚焦目标遮挡、尺寸过小和角度多等关键问题,综述现有方法的优缺点及难点.最后对无锚框目标检测方法中仍存在的问题进行总结并对未来发展的应用趋势进行展望.
