In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable...In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.展开更多
Wi-Fi指纹定位易受周围环境的影响,稳定性差;行人航迹推算(pedestrian dead reckoning,PDR)定位需要待定位目标的初始位置,且容易产生累计误差。针对上述问题,提出了一种基于PDR反馈的Wi-Fi室内定位算法。该算法主要分为三个阶段:基于...Wi-Fi指纹定位易受周围环境的影响,稳定性差;行人航迹推算(pedestrian dead reckoning,PDR)定位需要待定位目标的初始位置,且容易产生累计误差。针对上述问题,提出了一种基于PDR反馈的Wi-Fi室内定位算法。该算法主要分为三个阶段:基于相关向量回归(relevance vector regression,RVR)的初始位置定位阶段、基于PDR定位的反馈阶段、基于K近邻(K-nearest neighbor,KNN)的指纹定位阶段。实验结果表明,提出的算法在定位精度和稳定性方面较其他的定位算法有明显的提高,并且该算法相对于Wi-Fi定位减小了时间复杂度,实时性较好。展开更多
Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social ...Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social life.Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research(e.g.,studies on social anxiety).Methods:To search for potentially relevant biomarkers of FNE in human brain,the current study applied multivariate relevance vector regression,a machine-learning and data-driven approach,on brain morphological features(e.g.,cortical thickness)derived from structural imaging data;further,we used these features as indexes to predict self-reported FNE score in each participant.Results:Our results confirm the predictive power of multiple brain regions,including those engaged in negative emotional experience(e.g.,amygdala,insula),regulation and inhibition of emotional feeling(e.g.,frontal gyrus,anterior cingulate gyrus),and encoding and retrieval of emotional memory(e.g.,posterior cingulate cortex,parahippocampal gyrus).Conclusions:The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems,from primitive subcortical mechanisms to sophisticated cortical processes.展开更多
文摘In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.
基金This work was supported by the National Natural Science Foundation of China(Nos.31900757,32071083 and 32020103008)the Major Program of the Chinese National Social Science Foundation(No.17ZDA324)the Youth Innovation Promotion Association,CAS(No.2019088).
文摘Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social life.Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research(e.g.,studies on social anxiety).Methods:To search for potentially relevant biomarkers of FNE in human brain,the current study applied multivariate relevance vector regression,a machine-learning and data-driven approach,on brain morphological features(e.g.,cortical thickness)derived from structural imaging data;further,we used these features as indexes to predict self-reported FNE score in each participant.Results:Our results confirm the predictive power of multiple brain regions,including those engaged in negative emotional experience(e.g.,amygdala,insula),regulation and inhibition of emotional feeling(e.g.,frontal gyrus,anterior cingulate gyrus),and encoding and retrieval of emotional memory(e.g.,posterior cingulate cortex,parahippocampal gyrus).Conclusions:The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems,from primitive subcortical mechanisms to sophisticated cortical processes.
