A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The...
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A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The conjugate gradient method is accelerated with an approximate inverse matrix preconditioner obtained from a linear combination of matrix-valued Chebyshev polynomials. This implementation is tested on a Sun SMP machine. Since conjugate gradient method and preconditioner contain mainly matrix-vector and matrix-matrix multiplications, convincing results are obtained in terms of both speed and scalability.
A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The...
详细信息
A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The conjugate gradient method is accelerated with an approximate inverse matrix preconditioner obtained from a linear combination of matrix-valued Chebyshev polynomials. This implementation is tested on a Sun SMP machine. Since conjugate gradient method and preconditioner contain mainly matrix-vector and matrix-matrix multiplications, convincing results are obtained in terms of both speed and scalability.
Dynamic programming (DP) is a principled way to design optimal controllers for certain classes of nonlinear systems;unfortunately, DP is computationally very expensive. The Reinforcement Learning methods known as Adap...
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Dynamic programming (DP) is a principled way to design optimal controllers for certain classes of nonlinear systems;unfortunately, DP is computationally very expensive. The Reinforcement Learning methods known as Adaptive Critics (AC) provide computationally feasible means for performing approximate Dynamic programming (ADP). The term 'adaptive ' in A C refers to the critic 's improved estimations of the Value Function used by DP. To apply DP, the user must craft a Utility function that embodies all the problem-specific design specifications/criteria. Model Reference Adaptive Control methods have been successfully used in the control community to effect on-line redesign of a controller in response to variations in plant parameters, with the idea that the resulting closed loop system dynamics will mimic those of a Reference Model. The work reported here 1) uses a reference model in ADP as the key information input to the Utility function, and 2) uses ADP off-line to design the desired controller. Future work will extend this to on-line application. This method is demonstrated for a hypersonic shaped airplane called LoFL YTE®;its handling characteristics are natively a little "hotter" than a pilot would desire. A control augmentation subsystem is designed using ADP to make the plane "feel like " a better behaved one, as specified by a Reference Model. The number of inputs to the successfully designed controller are among the largest seen in the literature to date.
In this paper we analyze the use of research activities as learning instrument in electrical engineering and computer science. This pedagogic approach was applied in undergraduate disciplines, undergraduate teaching a...
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In this paper we analyze the use of research activities as learning instrument in electrical engineering and computer science. This pedagogic approach was applied in undergraduate disciplines, undergraduate teaching assistance and undergraduate research projects. Our main goals are optimize the learning process using research and motivate the use of research activities as learning instrument.
We propose sink insertion as a new technique to improve the mesh quality of Delaunay triangulations. We compare it with the conventional circumcenter insertion technique under three scheduling regimes: incremental, in...
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Adaptive critics have shown much promise for designing optimal nonlinear controllers in an off-line context. Still, their greatest potential exists in the context Of reconfigurable control, that is, real time controll...
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Adaptive critics have shown much promise for designing optimal nonlinear controllers in an off-line context. Still, their greatest potential exists in the context Of reconfigurable control, that is, real time controller redesign in response to (substantial) changes in plant dynamics. To accomplish this, a framework is proposed for the application of adaptive critics in real-time control (for those critic methods requiring a model of the plant). The framework is presented in the context of work being done in reconfigurable flight control by the NW computational Intelligence Lab (NWCIL) at Portland State University. The proposal incorporates recent work (by others) in fast and efficient on-line plant identification, considerations for bounding the computational costs of converging neural networks, and a novel approach (by us) toward the task of assuring system stability during the adaptation process. The potential and limitations of the proposed framework are discussed. It is suggested that with the recent rapid reduction in computational barriers, only certain theoretical issues remain as the central barriers to successful on-line application of the methods.
The approximate dynamic programming method known as Dual Heuristic programming (DHP) is applied to the design of a fuzzy controller for a 4-wheel, 2-axle vehicle. This controller is designed to guide an autonomous aut...
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The approximate dynamic programming method known as Dual Heuristic programming (DHP) is applied to the design of a fuzzy controller for a 4-wheel, 2-axle vehicle. This controller is designed to guide an autonomous automobile on a curved road while maintaining lateral acceleration at comfortable levels.
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