With the propagation of applications on the internet, the internet has become a great information source which supplies users with valuable information. But it is hard for users to quickly acquire the right informatio...
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With the propagation of applications on the internet, the internet has become a great information source which supplies users with valuable information. But it is hard for users to quickly acquire the right information on the web. This paper an intelligent agent for internet applications to retrieve and extract web information under user's guidance. The intelligent agent is made up of a retrieval script to identify web sources, an extraction script based on the document object model to express extraction process, a data translator to export the extracted information into knowledge bases with frame structures, and a data reasoning to reply users' questions. A GUI tool named Script Writer helps to generate the extraction script visually, and knowledge rule databases help to extract wanted information and to generate the answer to questions.
We study the effects of degree correlations on the evolution of cooperation in the prisoner's dilemma game with individuals located on two types of positively correlated networks. It is shown that the positive deg...
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We study the effects of degree correlations on the evolution of cooperation in the prisoner's dilemma game with individuals located on two types of positively correlated networks. It is shown that the positive degree correlation can either promote or inhibit the emergence of cooperation depending on network configurations. Furthermore, we investigate the probability to cooperate as a function of connectivity degree, and find that high-degree individuals generally have a higher tendency to cooperate. Finally, it is found that small-degree individuals usually change their strategy more frequently, and such change is shown to be unfavourable to cooperation for both kinds of networks.
This paper considers the sensitivity analysis of linear timeinvariant multivariable 2-D systems. Procedures that can be easily programmed on a digital computer are presented for the determination of the deviations tha...
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This paper considers the sensitivity analysis of linear timeinvariant multivariable 2-D systems. Procedures that can be easily programmed on a digital computer are presented for the determination of the deviations that are caused by the system's parameter changes about their nominal values of: 1) eigenvalues, 2) coefficients of the characteristic polynomial, and 3) the coefficient matrices of the numerator of the transfer function.
This paper considers the problem of matching a system under control to a model using output feedback when both the given system and the model are described by nonparametric models, such as the impulse response matrix....
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This paper considers the problem of matching a system under control to a model using output feedback when both the given system and the model are described by nonparametric models, such as the impulse response matrix. Conditions are established for exact matching, the problem being reduced to that of solving a linear system of equations. The present paper constitutes the first results in non-parametric model matching which appear to be very suitable for practical applications.
This paper presents a novel approach to artificial situation awareness for an autonomous vehicle operating in complex dynamic environments populated by other agents. A key aspect of situation awareness is the use of m...
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This paper handles pipe leakage in water networks through a fault detection and isolation mechanism coupled with dictionary learning strategies. We consider sparse representation strategies for sensor placement and su...
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The minimal model structure detection (MMSD) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidat...
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The minimal model structure detection (MMSD) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidate terms would involve 2.43 x 10(18) possible paths, it is shown that this can typically be reduced to 2 x 10(3) by augmenting the orthogonal estimation algorithm with genetic search procedures. The MMSD algorithm provides the first practical solution for optimal structure detection in NARMAX modelling, training neural networks and fuzzy systems modelling. Based on the MMSD algorithm, a refined forward regression orthogonal (RFRO) algorithm is developed. The RFRO algorithm initially detects a parsimonious model structure using the forward regression orthogonal algorithm and then refines the model structure by applying the MMSD algorithm to the reduced model term set. The RFRO algorithm cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algorithm and can find a smaller model than the forward regression orthogonal algorithm.
This study presents a new algorithm for nonlinear rational model identification. The new algorithm consists of a two-step procedure: a nonlinear rational function smoother is initially designed and used to smooth the ...
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This study presents a new algorithm for nonlinear rational model identification. The new algorithm consists of a two-step procedure: a nonlinear rational function smoother is initially designed and used to smooth the data, system identification is then performed based on the smoothed signal. By using the smoothed signal instead of the raw data, the severe noise problems which arise in rational model identification are avoided. The new approach significantly simplifies the procedure for dynamic nonlinear rational model identification, compared with earlier estimators, and provides unbiased estimates with the same degree of accuracy.
This paper proposes a robust method for automatic tuning of parameters of a discrete PID controller. The tuning rules for SISO and MIMO systems are based on automatic determination of critical gain and critical freque...
This paper proposes a robust method for automatic tuning of parameters of a discrete PID controller. The tuning rules for SISO and MIMO systems are based on automatic determination of critical gain and critical frequency from the estimated model parameters. The plant model can be expressed by a transfer function in continuous and/or discrete form or by differential and/or difference equation. A simple control law using Takahashi discrete form is proposed. Simulations results prove that it is easy to use being able to handle minimum and nonminimum phase plant as well.
In this paper, we propose an adaptive forward-backward-forward splitting algorithm for finding a zero of a pseudo-monotone operator which is split as a sum of three operators: the first is continuous single-valued, th...
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