The global and local stability of process systems in generalized Lotka-Volterra form is studied in this paper using entropy-like and quadratic Lyapunov function candidates. The global stability check for LV models is ...
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The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al., 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptoti...
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In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a ...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance th...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance theory (ART), have shown promising results in this task. Most of the existing neural network based document clustering techniques rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features applied to our proposed document clustering technique, called hierarchical SOMART (HSOMART), which is a hierarchical network built up from independent SOM and ART neural networks. We describe a phrase grammar extraction technique, and the proposed HSOMART. The experimental results of clustering documents from the REUTERS corpus using the extracted phrases as features show an improvement in the clustering performance evaluated using the entropy and F-measure.
In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite mem...
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ISBN:
(纸本)0780387309
In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.
Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension o...
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Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. Here we give new results on the relatively open problem of the design of physically based control laws. These results are for the sub-class of so-called discrete linear repetitive processes, which arise in applications areas such as iterative learning control.
Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by dire...
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Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or two-dimensional (2D) systems theory. Here, we give new results on the relatively open problem of the design of physically based control laws using an H/sub /spl infin// setting. These results are for the sub-class of so-called differential linear repetitive processes, which arise in application areas such as iterative learning control.
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