This study is an effort to give a practical solution in the problem of optimizing the structure of the hierarchical mixture of experts model, which is a natural extension of the associative Gaussian mixture of experts...
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This study is an effort to give a practical solution in the problem of optimizing the structure of the hierarchical mixture of experts model, which is a natural extension of the associative Gaussian mixture of experts system. We present two novel methods for optimizing such structures using genetic algorithms. Special concern is taken for reducing the computational time so as to efficiently allow the structure to "grow" while it evolves with the genetic algorithm. The main contribution of the paper lies on the efficient, topologically oriented, representations of such architectures so as to be optimized through involving genetic algorithms.
This paper presents the preliminary design and experimental results of a standard AA size vibration-induced micro energy transducer which is integrated with a power-management circuit. The transducer is a spring mass ...
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This paper presents the preliminary design and experimental results of a standard AA size vibration-induced micro energy transducer which is integrated with a power-management circuit. The transducer is a spring mass system which uses SU-8 molding and MEMS electroplating technologies fabricated copper springs to convert mechanical energy into electrical power by Faraday's law of induction. We have shown that when the MPG is packaged into an AA battery size container along with a power-management circuit that consists of rectifiers and a capacitor, it is capable of producing /spl sim/1.6 V DC when charged for less than 1 min. Potential applications for this micro power generator to serve as a power supply for a wireless temperature sensing system was proved to be possible with input frequencies at about 100 Hz and amplitudes be approximately 250 microns.
In an Internet-based control system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper extends the remote supervisory scheme b...
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ISBN:
(纸本)4907764227
In an Internet-based control system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper extends the remote supervisory scheme by Lee and Hsu (2003) to a modular one so as to reduce the supervisor synthesis complexity. Also, remote human issued commands are guaranteed to meet required specifications. A rapid thermal process in semiconductor manufacturing systems is provided to show the practicability of the proposed approach.
The paper is devoted to the problem of modeling demand for inventory management of slow-moving items in the case of reporting errors. It is proposed a generalization of the beta-binomial demand model that takes into a...
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The paper is devoted to the problem of modeling demand for inventory management of slow-moving items in the case of reporting errors. It is proposed a generalization of the beta-binomial demand model that takes into account possible reporting errors in the learning sample. For the new model, there are developed identification and forecasting algorithms that provide consistent estimators of the model parameters and mean square optimal forecasts. The efficiency of the proposed approach is illustrated by an application example for slow-moving car parts.
New developments in computer networks and communications provide new possibilities for control purposes. controlsystems for highly complex plants are themselves very complex and heterogeneous. A new software infrastr...
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Repetitive processes are a distinct class of 2D systems of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard or 2D systems theo...
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In this paper a new robust steepest-descent algorithm for discrete-time iterative learning control is introduced for plant models with multiplicative uncertainty. A theoretical analysis of the algorithm shows that if ...
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In this paper a new robust steepest-descent algorithm for discrete-time iterative learning control is introduced for plant models with multiplicative uncertainty. A theoretical analysis of the algorithm shows that if a tuning parameter in the algorithm is selected to be sufficiently large, the algorithm will result in monotonic convergence if the plant uncertainty satisfies a positivity condition. This is a major improvement when compared to the standard steepest-descent algorithm, which lacks a mechanism for finding a balance between convergence speed and robustness. Experimental work on a gantry robot is performed to demonstrate that the algorithm results in near perfect tracking in the limit.
This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the...
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The zero dynamics of simple continuous and fed-batch biore-actors is investigated in this paper for different input and output selections. A function λ generating the necessary coordinates transformation has been det...
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