This paper presents a robust fault detection system (FDS) for dynamic systems with unmodeled dynamics. In the FDS, umnodeled dynamics is first qualified as soft bound, which as well as model parameters are estimated u...
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This paper presents a robust fault detection system (FDS) for dynamic systems with unmodeled dynamics. In the FDS, umnodeled dynamics is first qualified as soft bound, which as well as model parameters are estimated using a robust identification algorithm. Then as a fault detection index, Kullback discrimination information (KDI) is derived into a feasible form and an index of umnodeled dynamics is also introduced. A decision making scheme is thus developed so that fault detection is carried out based on the KDI, the index of umnodeled dynamics and other prior information about the system.
In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to gener...
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In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary random image sequences. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Due to the reversibility of a reversible DTCNN, the same reversible DTCNN is used to recover the copyright label from a labelled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. computer simulation results are presented.
The aim of this work is the design of a class of neural networks for nonlinear function identification: the so-called neural modules. A neural module is a neural network with an internal structure specially designed t...
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The aim of this work is the design of a class of neural networks for nonlinear function identification: the so-called neural modules. A neural module is a neural network with an internal structure specially designed to be able to learn and mimic the behaviour of a certain class of dynamic systems. Neural networks are abstract models well suited for approximating nonlinear functions. The training cost and the structural complexity of neural networks can be drastically reduced if a-priori knowledge of the function to be learned is internally incorporated in the form of structural constraints. The resulting neural network has less parameters than a conventional one, much faster learning convergence and it can provide meaningful information about the learned nonlinear function. This paper describes the design of a useful set of neural modules for system identification and gives general guidelines for the design of neural modules. The resulting networks are evaluated and their use on general systems identification is pointed out.
In order to improve the resolution of spectrum analysis, the traditional method is to take more sampling periods, e. g. 50 sampling periods corresponding to the spectrum interval of I Hz. This method is not suitable t...
In order to improve the resolution of spectrum analysis, the traditional method is to take more sampling periods, e. g. 50 sampling periods corresponding to the spectrum interval of I Hz. This method is not suitable to the interharmonic measurement, because most interharmonics vary rather quickly with the time and so the analysis results based on long time sampling may be useless. In this paper the zero-padding technique with short sampling time is used to resolve interharmonics, and the zoom FFT is used to decrease the computation time in order to meet the needs of real-time measurement. The simulating computation shows that the resolution for two adjacent frequency components is usually sufficient to measure harmonics and interharmonics in power systems with acceptable short sampling and computation time. This method is also suited to analyse harmonics and interharmonics when there exists an undesirable asynchronous deviation between the sampling period and the signal period.
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation resu...
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation results and physical experimental results to verify the operation of the designs.
In this paper, global asymptotic stability of linear, time-variant, finite dimensional, zero input difference equations is investigated. We propose a technique that may be utilized to obtain regions of asymptotic stab...
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In this paper, global asymptotic stability of linear, time-variant, finite dimensional, zero input difference equations is investigated. We propose a technique that may be utilized to obtain regions of asymptotic stability in the coefficient space that incorporate information regarding the maximum rate of change of system parameters. Use of different matrix norms provide different ''shapes'' for the maximum allowable coefficient perturbations.
The task of guaranteeing that processes meet their deadlines is one of the most complex in the design of hard real-time control systems. This paper describes how process scheduling has been integrated into a framework...
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The task of guaranteeing that processes meet their deadlines is one of the most complex in the design of hard real-time control systems. This paper describes how process scheduling has been integrated into a framework of tools for the development of such systems. Functional specifications in the form of control system block diagrams are translated by the Framework into real-time source code which may be executed in a parallel processing environment. The automated off-line scheduling tool takes process specifications from the system under development and attempts to map these processes to the processors in such a way that all the system deadlines are met by using a detailed model of the run-time environment.
At the preparatory stage for connecting the Czech power systems to the West European UCPTE system, corrections for frequency must be included in the output power control, i.e. the primary control must be introduced. T...
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At the preparatory stage for connecting the Czech power systems to the West European UCPTE system, corrections for frequency must be included in the output power control, i.e. the primary control must be introduced. The result of the detailed analysis was a model of primary output power control of the block and a new power system control of Czech coal power plants. In the new power system control, an adaptive or robust controller will be used for operation stabilisation
The Development Framework provides a highly automatic translation from a specification to an implementation. The specification is in a popular, graphical controlengineering notation typically representing a system wi...
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The Development Framework provides a highly automatic translation from a specification to an implementation. The specification is in a popular, graphical controlengineering notation typically representing a system with stringent reliability requirements and hard real time constraints. An interface has been constructed between the Development Framework and the commercially available dependability modelling tool, SURF-2. This tool is designed to support an evaluation based design approach. Multiple design solutions can be compared to assess the implications of design decisions on the dependability of the system under development. The software demonstration will show how the interface between the Development Framework and SURF-2 is used to model the inclusion of selected fault tolerant mechanisms in the system under development.< >
In various adaptive estimation applications, the unknown channel is characterized by an impulse response which consists of extended regions of negligible response or 'inactivity'. We consider the LMS estimatio...
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In various adaptive estimation applications, the unknown channel is characterized by an impulse response which consists of extended regions of negligible response or 'inactivity'. We consider the LMS estimation of a channel that may be well approximated by an FIR (filter) model with only a few nonzero tap coefficients within a given delay horizon or tap length n. When the number of nonzero tap coefficients m is small compared to the delay horizon n, the performance of the LMS estimator is greatly enhanced when this specific structure is exploited. We propose a consistent algorithm that performs identification of nonzero taps only.
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