The problem of determining optimal observation strategies for identification of unknown parameters in distributed systems is discussed. Particularly, a setting where the measurement process is performed by collecting ...
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The problem of determining optimal observation strategies for identification of unknown parameters in distributed systems is discussed. Particularly, a setting where the measurement process is performed by collecting spatial data from mobile nodes with sensing capacity forming an organized network is considered. The framework is based on the Fisher information matrix as a measure of the information content in the measurements. The approach is to convert the problem to a canonical optimal control one in Mayer form, in which the control forces of the sensors may be optimized. Then, through an adaptation of some pairwise communication algorithms a numerical scheme is developed, which decompose the resulting problem and distributes the computational burden between the network nodes.
A sensor location problem for monitoring network with stationary nodes used for estimating unknown parameters of distributed-parameter system is addressed. In particular, the situation is considered, when the actual s...
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ISBN:
(纸本)9783902661555
A sensor location problem for monitoring network with stationary nodes used for estimating unknown parameters of distributed-parameter system is addressed. In particular, the situation is considered, when the actual spatial positions of sensor nodes at the experimentation stage may be uncertain to some extent and randomly uctuate around some locations specified at the configuration stage. In the presented approach, some results from experimental design theory for dynamic systems with random regressors are extended for the purpose of configuring a sensor network. Then, a simple algorithm based on the notion of approximate near minimum from statistical learning theory is adapted to select the most informative sensor locations. The delineated approach is illustrated by numerical example on a sensor network design for a twodimensional convective difiusion process.
The paper deals with the problem of robust fault diagnosis of industrial systems. The main objective was to develop fault diagnosis scheme based on the parameters identification via Bounded-Error Approach. The effecti...
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In this paper, an active FTC strategy is presented. First, it is developed in the context of linear systems and then it is extended to Takagi-Sugeno fuzzy systems. The key contribution of the proposed approach is an i...
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This note is a supplement to the previously published paper entitled "Stability analysis and the stabilization of a class of discrete-time dynamic neural networks" (Patan, 2007) and focuses on a complete for...
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This note is a supplement to the previously published paper entitled "Stability analysis and the stabilization of a class of discrete-time dynamic neural networks" (Patan, 2007) and focuses on a complete formulation of the minimum distance projection. The undertaken analysis results in the procedure defining the homogeneous projection operator and returning the feasible network parameters.
Repetitive processes propagate information in two independent directions where the duration of one is finite. They pose control problems that cannot be solved by application of results for other classes of 2D systems....
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Resource allocation problems are concerned with the allocation of limited resources among competing activities so as to achieve the best performances. However, in systems which serve many users there is a need to resp...
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Resource allocation problems are concerned with the allocation of limited resources among competing activities so as to achieve the best performances. However, in systems which serve many users there is a need to respect some fairness rules while looking for the overall *** concepts of multiple criteria equitable optimization can effectively be used to generate various fair and efficient allocation *** this paper we show how the importance weights allocated to several agents can be introduced into the fairness concepts and models.
Methods which provide good conditioning of model identification task in immune inspired, steady‐state controller SILO (Stochastic Immune Layer Optimizer) are presented in this paper. These methods are implemented in ...
Methods which provide good conditioning of model identification task in immune inspired, steady‐state controller SILO (Stochastic Immune Layer Optimizer) are presented in this paper. These methods are implemented in a model based optimization algorithm. The first method uses a safe model to assure that gains of the process’s model can be estimated. The second method is responsible for elimination of potential linear dependences between columns of observation matrix. Moreover new results from one of SILO implementation in polish power plant are presented. They confirm high efficiency of the presented solution in solving technical problems.
The paper studies the dependability of software implementation of the numerical Generalized Predictive control (GPC) Model Predictive control (MPC) algorithm. The algorithm is implemented for a control system of a mul...
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A method is developed to solve an optimal node activation problem in sensor networks whose measurements are supposed to be used to estimate unknown parameters of the underlying process model in the form of a partial d...
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A method is developed to solve an optimal node activation problem in sensor networks whose measurements are supposed to be used to estimate unknown parameters of the underlying process model in the form of a partial differential equation. Given a partition of the observation horizon into a finite number of consecutive intervals, the problem is set up to select nodes which will be active over each interval while the others will remain dormant such that the log-determinant of the resulting Fisher information matrix associated with the estimated parameters is maximized. The search for the optimal solution is performed using the branch-and-bound method in which an extremely simple and efficient technique is employed to produce an upper bound to the maximum objective function.
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