The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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a multilayer Recurrent neural network for solving continous time algebraic riccati equations
Neural Networks, 1998Co-Authors: Jun Wang, Guang WuAbstract:A multilayer Recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed Recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed Recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the Recurrent neural network and solvability of algebraic Riccati equations by use of the Recurrent neural network are discussed. The operating characteristics of the Recurrent neural network are also demonstrated through three illustrative examples.
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Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices
SIAM Journal on Scientific Computing, 1997Co-Authors: Jun WangAbstract:Three Recurrent neural networks are presented for computing the pseudoinverses of rank-deficient matrices. The first Recurrent neural network has the dynamical equation similar to the one proposed earlier for matrix inversion and is capable of Moore--Penrose inversion under the condition of zero initial states. The second Recurrent neural network consists of an array of neurons corresponding to a pseudoinverse matrix with decaying self-connections and constant connections in each row or column. The third Recurrent neural network consists of two layers of neuron arrays corresponding, respectively, to a pseudoinverse matrix and a Lagrangian matrix with constant connections. All three Recurrent neural networks are also composed of a number of independent subnetworks corresponding to the rows or columns of a pseudoinverse. The proposed Recurrent neural networks are shown to be capable of computing the pseudoinverses of rank-deficient matrices.
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Real-time synthesis of linear state observers using a multilayer Recurrent neural network
Proceedings of 1994 IEEE International Conference on Industrial Technology - ICIT '94, 1994Co-Authors: Jun Wang, Guang WuAbstract:Presents a multilayer Recurrent neural network for real-time synthesis of asymptotic state observers for linear dynamical systems. The proposed Recurrent neural network is composed of two layers of artificial neurons. By solving two matrix equations using the two-layer Recurrent neural network, the proposed Recurrent neural network is able to determine the output gain matrix of a Luenberger (asymptotic) state observer in real time. The proposed multilayer Recurrent neural network is shown to be capable of synthesizing asymptotic state observers with prespecified poles for linear time-varying dynamic systems. The operating characteristics of the Recurrent neural network for state observation are demonstrated by use of two illustrative examples. >
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A Recurrent neural network for computing pseudoinverse matrices
Mathematical and Computer Modelling, 1994Co-Authors: G. Wu, Jun Wang, J. HootmanAbstract:A Recurrent neural network is presented for computing pseudoinverse matrices. Under the zero initial state condition, the Recurrent neural network derived from a reflexive generalized inverse problem which involves two matrix equations can be used to solve the corresponding pseudoinverse problem which involves four matrix equations. The proposed Recurrent neural network based on the reflexive generalized inverse problem simplifies network dynamics and makes physical implementation easier. The proposed Recurrent neural network is proven and shown to be asymptotically stable and capable of computing pseudoinverse matrices. Three numerical examples are illustrated to show the performance of the proposed Recurrent neural network.
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Recurrent neural networks for solving linear matrix equations
Computers & Mathematics With Applications, 1993Co-Authors: Jun WangAbstract:Abstract Recurrent neural networks for solving linear matrix equations are proposed. The proposed Recurrent neural networks consist of two bidirectionally connected layers and each layer consists of an array of neurons. The proposed Recurrent neural networks are shown to be asymptotically stable in the large and capable of computing inverse matrices and solving Lyapunov matrix equations. The operating characteristics of the proposed Recurrent neural networks are demonstrated via several illustrative examples.
Guang Wu - One of the best experts on this subject based on the ideXlab platform.
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a multilayer Recurrent neural network for solving continous time algebraic riccati equations
Neural Networks, 1998Co-Authors: Jun Wang, Guang WuAbstract:A multilayer Recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed Recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed Recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the Recurrent neural network and solvability of algebraic Riccati equations by use of the Recurrent neural network are discussed. The operating characteristics of the Recurrent neural network are also demonstrated through three illustrative examples.
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Real-time synthesis of linear state observers using a multilayer Recurrent neural network
Proceedings of 1994 IEEE International Conference on Industrial Technology - ICIT '94, 1994Co-Authors: Jun Wang, Guang WuAbstract:Presents a multilayer Recurrent neural network for real-time synthesis of asymptotic state observers for linear dynamical systems. The proposed Recurrent neural network is composed of two layers of artificial neurons. By solving two matrix equations using the two-layer Recurrent neural network, the proposed Recurrent neural network is able to determine the output gain matrix of a Luenberger (asymptotic) state observer in real time. The proposed multilayer Recurrent neural network is shown to be capable of synthesizing asymptotic state observers with prespecified poles for linear time-varying dynamic systems. The operating characteristics of the Recurrent neural network for state observation are demonstrated by use of two illustrative examples. >
Benjamin T. Galen - One of the best experts on this subject based on the ideXlab platform.
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Recurrent Meningitis
Current Pain and Headache Reports, 2017Co-Authors: Jon Rosenberg, Benjamin T. GalenAbstract:Purpose of Review Recurrent meningitis is a rare clinical scenario that can be self-limiting or life threatening depending on the underlying etiology. This review describes the causes, risk factors, treatment, and prognosis for Recurrent meningitis. As a general overview of a broad topic, the aim of this review is to provide clinicians with a comprehensive differential diagnosis to aide in the evaluation and management of a patient with Recurrent meningitis. Recent Findings New developments related to understanding the pathophysiology of Recurrent meningitis are as scarce as studies evaluating the treatment and prevention of this rare disorder. A trial evaluating oral valacyclovir suppression after HSV-2 meningitis did not demonstrate a benefit in preventing recurrences. The data on prophylactic antibiotics after basilar skull fractures do not support their use. Intrathecal trastuzumab has shown promise in treating leptomeningeal carcinomatosis from HER-2 positive breast cancer. Monoclonal antibodies used to treat cancer and autoimmune diseases are new potential causes of drug-induced aseptic meningitis. Summary Despite their potential for causing Recurrent meningitis, the clinical entities reviewed herein are not frequently discussed together given that they are a heterogeneous collection of unrelated, rare diseases. Epidemiologic data on Recurrent meningitis are lacking. The syndrome of Recurrent benign lymphocytic meningitis described by Mollaret in 1944 was later found to be closely related to HSV-2 reactivation, but HSV-2 is by no means the only etiology of Recurrent aseptic meningitis. While the mainstay of treatment for Recurrent meningitis is supportive care, it is paramount to ensure that reversible and treatable causes have been addressed for further prevention.
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Recurrent Meningitis.
Current pain and headache reports, 2017Co-Authors: Jon Rosenberg, Benjamin T. GalenAbstract:Recurrent meningitis is a rare clinical scenario that can be self-limiting or life threatening depending on the underlying etiology. This review describes the causes, risk factors, treatment, and prognosis for Recurrent meningitis. As a general overview of a broad topic, the aim of this review is to provide clinicians with a comprehensive differential diagnosis to aide in the evaluation and management of a patient with Recurrent meningitis. New developments related to understanding the pathophysiology of Recurrent meningitis are as scarce as studies evaluating the treatment and prevention of this rare disorder. A trial evaluating oral valacyclovir suppression after HSV-2 meningitis did not demonstrate a benefit in preventing recurrences. The data on prophylactic antibiotics after basilar skull fractures do not support their use. Intrathecal trastuzumab has shown promise in treating leptomeningeal carcinomatosis from HER-2 positive breast cancer. Monoclonal antibodies used to treat cancer and autoimmune diseases are new potential causes of drug-induced aseptic meningitis. Despite their potential for causing Recurrent meningitis, the clinical entities reviewed herein are not frequently discussed together given that they are a heterogeneous collection of unrelated, rare diseases. Epidemiologic data on Recurrent meningitis are lacking. The syndrome of Recurrent benign lymphocytic meningitis described by Mollaret in 1944 was later found to be closely related to HSV-2 reactivation, but HSV-2 is by no means the only etiology of Recurrent aseptic meningitis. While the mainstay of treatment for Recurrent meningitis is supportive care, it is paramount to ensure that reversible and treatable causes have been addressed for further prevention.
Tuomas Jartti - One of the best experts on this subject based on the ideXlab platform.
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prednisolone reduces Recurrent wheezing after a first wheezing episode associated with rhinovirus infection or eczema
The Journal of Allergy and Clinical Immunology, 2007Co-Authors: Pasi Lehtinen, Timo Vanto, Aino Ruohola, Tytti Vuorinen, Olli Ruuskanen, Tuomas JarttiAbstract:Background Rhinovirus-induced early wheezing has been suggested as a new important risk factor for Recurrent wheezing. Objective We sought to investigate the risk factors for Recurrent wheezing and to determine post hoc the efficacy of prednisolone in risk groups. Methods We followed for 1 year 118 children (median age, 1.1 years) who had had their first episode of wheezing and had participated in a trial comparing prednisolone with placebo in hospitalized children. Demographics and laboratory data were obtained at study entry. The follow-up outcome was Recurrent wheezing (3 physician-confirmed episodes). Results Recurrent wheezing was diagnosed in 44 (37%) children. Independent risk factors were age P = .007 for interaction). Prednisolone was associated with less Recurrent wheezing in the rhinovirus group (0.19; 95% CI, 0.05-0.71), but not in the RSV (2.12; 95% CI, 0.46-9.76) or in the RSV/rhinovirus-negative groups (2.03; 95% CI, 0.83-5.00; P = .017 for interaction). Conclusion Rhinovirus-induced early wheezing is a major viral risk factor for Recurrent wheezing. Prednisolone may prevent Recurrent wheezing in rhinovirus-affected first-time wheezers. The presence of eczema may also influence the response to prednisolone. Clinical implications A prospective trial is needed to test the hypothesis that prednisolone reduces Recurrent wheezing in rhinovirus-affected wheezing children.
Yongan Zhou - One of the best experts on this subject based on the ideXlab platform.
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Intraoperative Recurrent laryngeal nerve monitoring: a useful method for patients with esophageal cancer
Diseases of The Esophagus, 2012Co-Authors: Daixing Zhong, Jinbo Zhao, Lianhong Chen, Yongan Zhou, Yu Hong Li, Yunjie Wang, Qingshu Cheng, Weiping Zhou, Xia LiAbstract:Summary It is well accepted that Recurrent laryngeal nerve paralysis is a severe complication of esophagectomy or lymphadenectomy performed adjacent to the Recurrent laryngeal nerves. Herein, determination of the effectiveness of implementing continuous Recurrent laryngeal nerve monitoring to reduce the incidence of Recurrent laryngeal nerve paralysis after esophagectomy was sought. A total of 115 patients diagnosed with esophageal cancer were enrolled in the thoracic section of the Tangdu Hospital of the Fourth Military Medical University from April 2008 to April 2009. Clinical parameters of patients, the morbidity, and the mortality following esophageal resection were recorded and compared. After the surgery, a 2-year follow up was completed. It was found that Recurrent laryngeal nerve paralysis and postoperative pneumonia were more frequently diagnosed in the patients that did not receive continuous Recurrent laryngeal nerve monitoring (6/61 vs. 0/54). Furthermore, positive mediastinal lymph nodes (P = 0.015), total mediastinal lymph nodes (P < 0.001), positive total lymph nodes (P = 0.027), and total lymph nodes (P < 0.001) were more often surgically removed in the patients with continuous Recurrent laryngeal nerve monitoring. These patients also had a higher 2-year survival rate (P = 0.038) after surgery. It was concluded that continuous intraoperative Recurrent laryngeal nerve monitoring is technically safe and effectively identifies the Recurrent laryngeal nerves. This may be a helpful method for decreasing the incidence of Recurrent laryngeal nerve paralysis and postoperative pneumonia, and for improving the efficiency of lymphadenectomy.
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Intraoperative Recurrent laryngeal nerve monitoring during surgery for left lung cancer
The Journal of Thoracic and Cardiovascular Surgery, 2010Co-Authors: Jinbo Zhao, Wenhai Li, Lianhong Chen, Daixing Zhong, Hui Xu, Yongan ZhouAbstract:Objective This study evaluated the safety and efficacy of intraoperative Recurrent laryngeal nerve monitoring during surgery for left lung cancer. Methods From April 2008 to April 2009, a total of 25 patients at high risk for left Recurrent laryngeal nerve injury agreed to and underwent intraoperative Recurrent laryngeal nerve monitoring during surgery for left lung cancer in our hospital. Results and clinical records were reviewed. Results All the patients' left Recurrent laryngeal nerves were identified during operation by intraoperative Recurrent laryngeal nerve monitoring. Twenty-four patients retained normal left Recurrent laryngeal nerves after the operation. One patient, in whom part of the left Recurrent laryngeal nerve was found to be invaded, underwent single-stage nerve anastomosis under Recurrent laryngeal nerve monitoring after the invaded nerve was resected. There were no significant intraoperative or postoperative complications among the other patients. Conclusions Intraoperative Recurrent laryngeal nerve monitoring during thoracotomy is a safe and effective way of identifying the nerve. It may help surgeons to avoid injuring the Recurrent laryngeal nerve during some thoracic procedures.
