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Markov chain approach to identifying Wiener systems

science china(Information {2.) 2012年第5期55卷 1201-1217页

作者： zhao wenxiao 1,2.& chen hanfu 1,{2.,institute of systems science,amss,chinese Academy of sciences,beijing 100190,china 2.National Center for Mathematics and Interdisciplinary sciences,chinese Academy of sciences,beijing 100190,china 1. key laboratory of {2. and control Institute of Systems Science AMSS Chinese Academy of Sciences Beijing 100190 China2. National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing 100190 China

Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is *** recursive estimates for unknown coefficients of the linear subsystem and for the values of the nonlinear function at any fixed points are given by the stochastic approx-imation algorithms with expanding truncations (SAAWET).With the help of properties of the Markov chain connected with the linear subsystem,all estimates derived in the paper are proved to be strongly *** comparison with the existing results on the topic,the method presented in the paper simplifies the convergence analysis and requires weaker conditions.A numerical example is given,and the simulation results are consistent with the theoretical analysis.

关键词： Wiener system recursive identification stochastic approximation Markov chain strong consis-tency

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Recursive Identification for Hammerstein System with ARX Subsystem

Recursive Identification for Hammerstein System with ARX Sub...

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第2.届中国控制会议

作者： zhao wenxiao, {2. hanfu Academy of Mathematics and systems science, chinese Academy of sciences, beijing 100080

＜正＞Identification is considered for the Hammerstein system consisting of a static nonlinear block f（·） followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f（·）. Recursive estimates are given for coefficients of the ARX subsystem and for the value of f（u） at any u. All estimates are convergent to the true values with probability one. Numerical examples are provided justifying the theoretical analysis.

关键词： Hammerstein system ARX nonparametric nonlinearity recursive estimate strong consistency stochastic approximation.

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Distributed dynamic stochastic approximation algorithm over time-varying networks

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Autonomous Intelligent systems 2021年第1期1卷 49-68页

作者： Kewei Fu Han-Fu chen wenxiao {2. key laboratory of {2. and control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190People’s Republic of China School of Mathematical sciences University of Chinese Academy of SciencesBeijing 100049People’s Republic of China

In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a *** agent updates its estimate by using the local observation,the dynamic information of the global root,and information received from its *** with similar works in optimization area,we allow the observation to be noise-corrupted,and the noise condition is much ***,instead of the upper bound of the estimate error,we present the asymptotic convergence result of the *** consensus and convergence of the estimates are ***,the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.

关键词： Distributed algorithm Dynamic stochastic approximation algorithm Time-varying network

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Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from chinese Association of Radiologists

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Intelligent Medicine 2022年第4期2卷 221-229页

作者： Longjiang Zhang zhao Shi Min chen Yingmin chen Jingliang cheng Li Fan Nan Hong wenxiao Jia Guihua Jiang Shenghong Ju Xiaogang Li Xiuli Li Changhong Liang Weihua Liao Shiyuan Liu Zaiming Lu Lin Ma Ke Ren Pengfei Rong Bin Song Gang Sun Rongpin Wang Zhibo Wen Haibo Xu Kai Xu Fuhua Yan Yizhou Yu Yunfei Zha Fandong Zhang Minwen Zheng Zhen Zhou Wenzhen Zhu Guangming Lu Zhengyu Jin on behalf of chinese Association of Radiologists Department of Radiology Affiliated Jinling HospitalMedical School of Nanjing UniversityNanjingJiangsu 210002China Department of Radiology Beijing HospitalNational Center of GerontologyBeijing 100005China Department of Radiology Hebei General HospitalShijiazhuangHebei 050199China Department of Magnetic Resonance Imaging The First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan 450052China Department of Medical Imaging and Nuclear Medicine Changzheng Hospital of Naval Medical UniversityShanghai 200072China Department of Radiology Peking University People’s HospitalBeijing 100044China Imaging Center First AffiliatedHospital of Xinjiang Medical UniversityUrumqiXinjiang Uygur Autonomous Region 830054China Department of Medical Imaging Guangdong Second Provincial General HospitalGuangzhouGuangdong 510050China Department of Radiology Zhongda HospitalSchool of MedicineSoutheast UniversityNanjingJiangsu 211189China Department of Radiology General Hospital of Northern Theater CommandShenyangLiaoning 110011China DeepWise AI lab.beijing 100089 China Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhouGuangdong 519041China Department of Radiology Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Radiology Shengjing Hospital of China Medical UniversityShenyangLiaoning 110001China Department of Radiology Chinese PLA(People’s Liberation Army)General HospitalBeijing 100853China Department of Radiology Xiang’an Hospital of Xiamen UniversitySchool of MedicineXiamen UniversityXiamenFujian 361005China Department of Radiology Third Xiangya HospitalCentral South UniversityChangshaHunan 410013China Department of Radiology Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceWest China HospitalSichuan UniversityChengduSichuan 610044China Department of Nuclear Medicine 960 Hospital of PLAJi’nanShandong 250012China Department of Radiology Guizhou Provincial People’s Hosp

In recent years,with the development of artificial intelligence,especially deep learning technology,researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical ***,because of the complexity and flexibility of the deep learning algorithms,these researches have great variability on model building,validation process,performance description and results *** lack of a reliable,consistent,standardized design protocol has,to a certain extent,affected the progress of clinical translation and technology development of computer aided detection *** reviewing a large number of literatures and extensive discussion with domestic experts,this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular *** further research and application expansion,this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.

关键词： Cerebrovascular diseases Deep learning Study design Medical imaging

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