作者：
Ming T. TsuangStephen V. ParaoneGuest EditorDr. Tsuang is the Stanley Cobb Professor of Psychiatry
Director Harvard Institute of Psychiatric Epidemiology and Genetics and Superintendent and Head Harvard Department of Psychiatry at Massachusetts Mental Health Center. He received an M.D. degree at the College of Medicine National Taiwan University and his Ph.D. at the Institute of Psychiatry Faculty of Medicine University of London. Dr. Tsuang also received a Doctor of Science (D.Sc.) in Psychiatric Epidemiology and Genetics Faculty of Science University of London. Professor Tsuang has worked throughout his career as a clinician teacher researcher and administrator in a variety of private state federal and international mental health care settings. He has been recognized both nationally and internationally for his research in schizophrenia and other major psychiatric disorders including manic-depressive illness and substance abuse. One of his areas of interest is in the rapidly developing field of research related to the interactions between genetic and environmental risk factors for severe mental disorders. Professor Tsuang is the recipient of a myriad of awards for his work among them the First Prize in Clinical Research Award from the American Academy of Clinical Psychiatrists
the Rema Lapouse Award for Mental Health Epidemiology from the American Public Health Association and the Stanley Dean Award for Basic Research in Schizophrenia from the American College of Psychiatrists. He is also a Member of the Institute of Medicine National Academy of Sciences and Member Academia Sinica of Taiwan. Recently Professor Tsuang received the Lifetime Achievement Award from the International Society of Psychiatric Genetics and the Taiwanese-American Award for Achievement in Science and Engineering. He has authored or co-authored nearly 400 publications including publications in peer reviewed journals in book chapters and also in books in the areas of psychiatric epidemiology psychiatric genetics nosology of major psychoses ne
A class of artificial neural networks with a two-layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to a...
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A class of artificial neural networks with a two-layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standarddynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.
This paper presents a comparative study of two multiperspective approaches to management, one developed and applied mainly in the United States, the other in China and East Asia. The contentions of the two approaches ...
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This paper presents a comparative study of two multiperspective approaches to management, one developed and applied mainly in the United States, the other in China and East Asia. The contentions of the two approaches are briefly outlined, commonality anddifferences analysed, cultural traditions surfaced, and the ways the two approaches inform and learn from each other reported. It is suggested that the recent development of multiperspective approaches world-wide is not accidental, but can be seen as an outcome of humankind's common search for responses to the increasingly challenging multidimensional complexity in human systems management. Systems/management scientists in both the West and the East can do better in dealing with the complexity if we consciously open to, inform and learn from each other.
Selected adaptive critic (AC) methods are known to be capable of designing (approximately) optimal control policies for nonlinear plants. The present research focuses on an AC method known as dual heuristic programmin...
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Selected adaptive critic (AC) methods are known to be capable of designing (approximately) optimal control policies for nonlinear plants. The present research focuses on an AC method known as dual heuristic programming (dHP). In particular it is seen as useful to explore correspondences between the form of a utility function and the resulting controllers designed by the dHP method. Based on the task of designing a steering controller for a 2-axle, terrestrial, autonomous vehicle, the present paper relates a pair of critics to "divide the labor" of training the controller. Improvements in convergence of the training process is realized in this way. The controllers designed by the dHP method are reasonably robust, anddemonstrate good performance on disturbances not even trained on encountering a patch of ice during a steering maneuver, and encountering a wind gust perpendicular to direction of travel.
作者：
Mann, William C.Department of Occupational Therapy
Ph.D. Program in Rehabilitation Science Rehabilitation Engineering Research Center on Aging University of Florida Box 100164 Gainesville Fl 32610-0164 USA. Tel.: +1 352 392 2617Fax: +1 352 846 1042
作者：
Richard Toon[a] Richard Toon is research scientist at the Arizona Science Center
Phoenix and a Ph.D. candidate in museum studies at the University of Leicester England. Before joining ASC he was a program consultant and evaluator in New York City to a variety of organizations in health care juvenile justice and education including Outward Bound USA Inc.
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Using findings from a federally mandated statewide study of homelessness in the state of Iowa, this paper presents methodologies developed to address various aspects of homeless research, including enumeration of the ...
Using findings from a federally mandated statewide study of homelessness in the state of Iowa, this paper presents methodologies developed to address various aspects of homeless research, including enumeration of the population, screening for reporting duplications, estimating the annual number of incidents of homelessness, and exploring county-level estimates of homelessness. After implementing an algorithm to eliminate duplicate reporting, and using the baseline unduplicated reported number of homeless persons, a statewide estimate of the number of homeless individuals was derived. Following further adjustments for differences in agency reporting practices and after extrapolating for nonreported time periods, we Estimated the number of unique incidents of homelessness experienced in the state during the year of the study (1997). The policy implications of these findings are discussed.
A variety of alternate training strategies for implementing the dual heuristic programming (dHP) method of approximate dynamic programming in the neurocontrol context are explored. The dHP method of controller trainin...
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A variety of alternate training strategies for implementing the dual heuristic programming (dHP) method of approximate dynamic programming in the neurocontrol context are explored. The dHP method of controller training has been successfully demonstrated by a number of authors on a variety of control problems in recent years, but no unified view of the implementation details of the method has yet emerged. A number of options are described for sequencing the training of the controller and critic networks in dHP implementations. Results are given about their relative efficiency and the quality of the resulting controllers for two benchmark control problems.
The roles of plant models in adaptive critic methods for approximate dynamic programming are considered, with primary focus given to the dynamic heuristic programming (dHP) methodology. For complete system identificat...
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The roles of plant models in adaptive critic methods for approximate dynamic programming are considered, with primary focus given to the dynamic heuristic programming (dHP) methodology. For complete system identification, partial, approximate, and qualitative models of plant dynamics are considered. Such models are found to be sufficient for successful controller design. As classification is in general easier than regression, the results for qualitative models suggest an avenue for simplifying ongoing system identification in adaptive control applications.
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