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Nonlinear model predictive control with relevance vector regression and particle swarm optimization

控制理论与应用（英文版） 2013年第4期11卷 563-569页

作者： M.GERMIN NISHA G.N.PILLAI Department of Electrical Engineering Indian Institute of Technology Roorkee

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.

关键词： relevance vector regression Least squares support vector machines Nonlinear model predictive control Particle swarm optimization with controllable random exploration velocity

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Decoding fear of negative evaluation from brain morphology:A machine-learning study on structural neuroimaging data

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Quantitative Biology 2022年第4期10卷 390-402页

作者： Chunliang Feng Frank Krueger Ruolei Gu Wenbo Luo Key Laboratory of Brain Cognition and Education Sciences(South China Normal UniversityMinistry of Education)Guangzhou 510631China Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science Center for Studies of Psychological ApplicationSchool of PsychologySouth China Normal UniversityGuangzhou 510631China School of Systems Biology George Mason UniversityFairfaxVA 22030USA Department of Psychology George Mason University of MannheimMannheim 68159Germany Key Laboratory of Behavioral Science Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China Research Center of Brain and Cognitive Neuroscience Liaoning Normal UniversityDalian 116029China

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 *** 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 ***: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.

关键词： fear of negative evaluation social anxiety structural magnetic resonance imaging machine learning relevance vector regression

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