Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due ...
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ISBN:
(纸本)9787894631046
Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due to different biochemical reactions in the genetic regulatory networks (GRNs) which are also affected by noises. Therefore, in this paper, we propose a GRN model that is subject to additive and multiplicative noises as well as time-varying delays. The time-varying delay is assumed to belong to an interval and no restriction on the derivative of the time-varying delay is needed, which allows the delay to be a fast time-varying function. Robust stochastic stability of such GRNs with disturbance attenuation is analyzed by applying the control theory and mathematical tools. Based on the Lyapunov method, new stability conditions are derived in the form of linear matrix inequalities (LMIs) that are dependent on the upper and lower bounds of time delays. An example is employed to illustrate the applicability and usefulness of the developed theoretical results.
This technical note addresses the robust H ∞ finite-horizon output feedback control problem for a class of uncertain discrete stochastic nonlinear time-varying systems with both sensor and actuator saturations. In t...
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This technical note addresses the robust H ∞ finite-horizon output feedback control problem for a class of uncertain discrete stochastic nonlinear time-varying systems with both sensor and actuator saturations. In the system under investigation, all the system parameters are allowed to be time-varying, the parameter uncertainties are assumed to be of the polytopic type, and the stochastic nonlinearities are described by statistical means which can cover several classes of well-studied nonlinearities. The purpose of the problem addressed is to design an output feedback controller, over a given finite-horizon, such that the H ∞ disturbance attenuation level is guaranteed for the nonlinear stochastic polytopic system in the presence of saturated sensor and actuator outputs. Sufficient conditions are first established for the robust H ∞ performance through intensive stochastic analysis, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired output feedback controller achieving the prescribed H ∞ disturbance rejection level. Simulation results demonstrate the effectiveness of the developed controller design scheme.
In this paper, the robust finite-horizon filtering problem is investigated for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints....
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ISBN:
(纸本)9781424451951
In this paper, the robust finite-horizon filtering problem is investigated for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. The stochastic nonlinearities are described by statistical means which can cover several classes of well-studied nonlinearities. The measurement missing phenomenon is also considered. Sufficient conditions are derived for a finite-horizon filter to satisfy the estimation error variance constraints. These conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities (RLMIs). An illustrative simulation example is given to show the the effectiveness of the proposed algorithm.
Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due ...
详细信息
Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due to different biochemical reactions in the genetic regulatory networks (GRNs) which are also affected by noises. Therefore, in this paper, we propose a GRN model that is subject to additive and multiplicative noises as well as time-varying delays. The time-varying delay is assumed to belong to an interval and no restriction on the derivative of the time-varying delay is needed, which allows the delay to be a fast time-varying function. Robust stochastic stability of such GRNs with disturbance attenuation is analyzed by applying the control theory and mathematical tools. Based on the Lyapunov method, new stability conditions are derived in the form of linear matrix inequalities (LMIs) that are dependent on the upper and lower bounds of time delays. An example is employed to illustrate the applicability and usefulness of the developed theoretical results.
In data warehousing, ETL (Extract, Transform, and Load) processes take charge of extracting the data from data sources that would be contained in the data warehouse. Due to their relevance, the quality of these proces...
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ISBN:
(纸本)9781605588162
In data warehousing, ETL (Extract, Transform, and Load) processes take charge of extracting the data from data sources that would be contained in the data warehouse. Due to their relevance, the quality of these processes should be formally assessed since the early stages of development, in order to avoid making bad decisions as a result of incorrect data. In this paper, a set of measures to evaluate the structural complexity of ETL process models at conceptual level is presented. Moreover, this study is accompanied by four experiments whose aim is the empirical validation of the proposed measures. The main advantage of this approach is the early evaluation of ETL process models. This early evaluation support designers in their maintenance tasks. This proposal is based on UML (Unifield Modeling Language) activity diagrams for modeling ETL processes and the adoption of the FMESP (Framework for the Modeling and Evaluation of Software Processes) framework. Copyright 2009 ACM.
Data warehouses (DW) integrate different data sources in order to give a multidimensional view of them to the decision-maker. To this aim, the ETL (Extraction, Transformation and Load) processes are responsible for ex...
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ISBN:
(纸本)9781605588018
Data warehouses (DW) integrate different data sources in order to give a multidimensional view of them to the decision-maker. To this aim, the ETL (Extraction, Transformation and Load) processes are responsible for extracting data from heterogeneous operational data sources, their transformation (conversion, cleaning, standardization, etc.), and its load in the DW. In recent years, several conceptual modeling approaches have been proposed for designing ETL processes. Although these approaches are very useful for documenting ETL processes and supporting the designer tasks, these proposals fail to give mechanisms to carry out an automatic code generation stage. Such a stage should be required to both avoid fails and save development time in the implementation of complex ETL process. Therefore, in this paper we define an approach for the automatic code generation of ETL processes. To this aim, we align the modeling of ETL processes in DW with MDA (Model Driven Architecture) by formally defining a set of QVT (Query, View, Transformation) transformations. Copyright 2009 ACM.
This paper is concerned with the H infin fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the...
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This paper is concerned with the H infin fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H infin fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H infin performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method.
This paper investigates the H control problem for a class of systems with repeated scalar nonlinearities and multiple packet *** nonlinear system is described by a discrete-time state equation involving repeated scala...
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This paper investigates the H control problem for a class of systems with repeated scalar nonlinearities and multiple packet *** nonlinear system is described by a discrete-time state equation involving repeated scalar *** multiple packet-dropout phenomenon is assumed to occur in both the senor-to-controller and the controller-to-actuator channels, and the data missing law for each individual sensor/actuator satisfies individual probabilistic distribution in the interval [0 1].An observer-based feedback controller is designed to stochastically stabilize the closed-loop control system and preserves a guaranteed H *** examples are provided to show the applicability of the proposed theoretical results.
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